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• The radiomics model showed a good diagnostic effect in HOS patients. • The clinical model performed well in predicting the Huvos grade in HOS patients. • The combined model is apt for Huvos grading after preoperative HOS chemotherapy. To develop a model combining clinical and radiomics features from CT scans for a preoperative noninvasive evaluation of Huvos grading of neoadjuvant chemotherapy in patients with HOS. 183 patients from center A and 42 from center B were categorized into training and validation sets. Features derived from radiomics were obtained from unenhanced CT scans.Following dimensionality reduction, the most optimal features were selected and utilized in creating a radiomics model through logistic regression analysis. Integrating clinical features, a composite clinical radiomics model was developed, and a nomogram was constructed. Predictive performance of the model was evaluated using ROC curves and calibration curves. Additionally, decision curve analysis was conducted to assess practical utility of nomogram in clinical settings. LASSO LR analysis was performed, and finally, three selected image omics features were obtained.Radiomics model yielded AUC values with a good diagnostic effect for both patient sets (AUCs: 0.69 and 0.68, respectively). Clinical models (including sex, age, pre-chemotherapy ALP and LDH levels, new lung metastases within 1 year after surgery, and incidence) performed well in terms of Huvos grade prediction, with an AUC of 0.74 for training set. The AUC for independent validation set stood at 0.70. Notably, the amalgamation of radiomics and clinical features exhibited commendable predictive prowess in training set, registering an AUC of 0.78. This robust performance was subsequently validated in the independent validation set, where the AUC remained high at 0.75. Calibration curves of nomogram showed that the predictions were in good agreement with actual observations. Combined model can be used for Huvos grading in patients with HOS after preoperative chemotherapy, which is helpful for adjuvant treatment decisions. [ABSTRACT FROM AUTHOR]

• Generative models can improve radiomics performance in different tasks when radiomics extracted from low dose CTs. • Simulation paired low-high dose CTs trained generative models can be used to denoise low dose CT without re-training. • Generative models can improve AUC by 0.05 of radiomics in survival predication and lung cancer diagnosis. • Denoising using generative models seems to be a necessary pre-processing step for radiomic features from low dose CTs. Radiomics is an active area of research focusing on high throughput feature extraction from medical images with a wide array of applications in clinical practice, such as clinical decision support in oncology. However, noise in low dose computed tomography (CT) scans can impair the accurate extraction of radiomic features. In this article, we investigate the possibility of using deep learning generative models to improve the performance of radiomics from low dose CTs. We used two datasets of low dose CT scans – NSCLC Radiogenomics and LIDC-IDRI – as test datasets for two tasks – pre-treatment survival prediction and lung cancer diagnosis. We used encoder-decoder networks and conditional generative adversarial networks (CGANs) trained in a previous study as generative models to transform low dose CT images into full dose CT images. Radiomic features extracted from the original and improved CT scans were used to build two classifiers – a support vector machine (SVM) and a deep attention based multiple instance learning model – for survival prediction and lung cancer diagnosis respectively. Finally, we compared the performance of the models derived from the original and improved CT scans. Denoising with the encoder-decoder network and the CGAN improved the area under the curve (AUC) of survival prediction from 0.52 to 0.57 (p-value < 0.01). On the other hand, the encoder-decoder network and the CGAN improved the AUC of lung cancer diagnosis from 0.84 to 0.88 and 0.89 respectively (p-value < 0.01). Finally, there are no statistically significant improvements in AUC using encoder-decoder networks and CGAN (p-value = 0.34) when networks trained at 75 and 100 epochs. Generative models can improve the performance of low dose CT-based radiomics in different tasks. Hence, denoising using generative models seems to be a necessary pre-processing step for calculating radiomic features from low dose CTs. [ABSTRACT FROM AUTHOR]

• Introduction of the FAIR data principles in radiotherapy. • Artificial Intelligence applications begin to manifest themselves in radiotherapy. • Examples of FAIR data in radiotherapy. • Stakeholders action items for the implementation of FAIR principles. Given the rapid growth of artificial intelligence (AI) applications in radiotherapy and the related transformations toward the data-driven healthcare domain, this article summarizes the need and usage of the FAIR (Findable, Accessible, Interoperable, Reusable) data principles in radiotherapy. This work introduces the FAIR data concept, presents practical and relevant use cases and the future role of the different parties involved. The goal of this article is to provide guidance and potential applications of FAIR to various radiotherapy stakeholders, focusing on the central role of medical physicists. [ABSTRACT FROM AUTHOR]

• Description of ontologies for medical uses. • Examples of ontology applications in radiation therapy. • Ontologies as basis for communication protocols. • Efforts by American Association of Medical Physicists to construct an ontology. Ontologies are a formal, computer-compatible method for representing scientific knowledge about a given domain. They provide a standardized vocabulary, taxonomy and set of relations between concepts. When formatted in a standard way, they can be read and reasoned upon by computers as well as by humans. At the 2019 International Conference on the Use of Computers in Radiation Therapy, there was a session devoted to ontologies in radiation therapy. This paper is a compilation of the material presented, and is meant as an introduction to the subject. This is done by means of a didactic introduction to the topic followed by a series of applications in radiation therapy. The goal of this article is to provide the medical physicist and related professionals with sufficient background that they can understand their construction as well as their practical uses. [ABSTRACT FROM AUTHOR]

• We investigated the presence of volume-confounding effects in radiomics features. • Our results show that many radiomics features are volume-confounded. • We offered the radiomics community a workflow for introducing safeguards in signature developments. 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. [ABSTRACT FROM AUTHOR]

• Designed automated data analysis pipelines with varied user interaction requirements. • Integrated clinical, imaging and RT treatment information for H&N LRF prediction. • Insights regarding user-driven pipelines and unintended biases. Precision cancer medicine is dependent on accurate prediction of disease and treatment outcome, requiring integration of clinical, imaging and interventional knowledge. User controlled pipelines are capable of feature integration with varied levels of human interaction. In this work we present two pipelines designed to combine clinical, radiomic (quantified imaging), and RTx-omic (quantified radiation therapy (RT) plan) information for prediction of locoregional failure (LRF) in head and neck cancer (H&N). Pipelines were designed to extract information and model patient outcomes based on clinical features, computed tomography (CT) imaging, and planned RT dose volumes. We predict H&N LRF using: 1) a highly user-driven pipeline that leverages modular design and machine learning for feature extraction and model development; and 2) a pipeline with minimal user input that utilizes deep learning convolutional neural networks to extract and combine CT imaging, RT dose and clinical features for model development. Clinical features with logistic regression in our highly user-driven pipeline had the highest precision recall area under the curve (PR-AUC) of 0.66 (0.33–0.93), where a PR-AUC = 0.11 is considered random. CONCLUSIONS: Our work demonstrates the potential to aggregate features from multiple specialties for conditional-outcome predictions using pipelines with varied levels of human interaction. Most importantly, our results provide insights into the importance of data curation and quality, as well as user, data and methodology bias awareness as it pertains to result interpretation in user controlled pipelines. [ABSTRACT FROM AUTHOR]

In recent years, the American Society for Radiation Oncology (ASTRO) has received requests for a standard list of data elements from other societies, database architects, Electronic Health Record vendors and, most recently, the pharmaceutical industry. These requests point to a growing interest in capturing radiation oncology data within registries and for quality measurement, interoperability initiatives, and research. Identifying a short and consistent list will lead to improved care coordination, a reduction in data entry by practice staff, and a more complete view of the holistic approach required for cancer treatment. The task force formulated recommendations based on analysis from radiation specific data elements currently in use in registries, accreditation programs, incident learning systems, and electronic health records. The draft manuscript was peer reviewed by 8 reviewers and ASTRO legal counsel and was revised accordingly and posted on the ASTRO website for public comment in April 2019 for 2 weeks. The final document was approved by the ASTRO Board of Directors in June 2019. [ABSTRACT FROM AUTHOR]

• ADC intensity-based radiomic features are less sensitive to manual tumour delineation differences and pre-processing. • Shape features appear to be strongly affected by delineation quality. • Overall, textural features appear to be poorly or moderately reproducible with respect to the image pre-processing. 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. [ABSTRACT FROM AUTHOR]

Background and purpose A rapid learning approach has been proposed to extract and apply knowledge from routine care data rather than solely relying on clinical trial evidence. To validate this in practice we deployed a previously developed decision support system (DSS) in a typical, busy clinic for non-small cell lung cancer (NSCLC) patients. Material and methods Gender, age, performance status, lung function, lymph node status, tumor volume and survival were extracted without review from clinical data sources for lung cancer patients. With these data the DSS was tested to predict overall survival. Results 3919 lung cancer patients were identified with 159 eligible for inclusion, due to ineligible histology or stage, non-radical dose, missing tumor volume or survival. The DSS successfully identified a good prognosis group and a medium/poor prognosis group (2 year OS 69% vs. 27/30%, p < 0.001). Stage was less discriminatory (2 year OS 47% for stage I–II vs. 36% for stage IIIA–IIIB, p = 0.12) with most good prognosis patients having higher stage disease. The DSS predicted a large absolute overall survival benefit (∼40%) for a radical dose compared to a non-radical dose in patients with a good prognosis, while no survival benefit of radical radiotherapy was predicted for patients with a poor prognosis. Conclusions A rapid learning environment is possible with the quality of clinical data sufficient to validate a DSS. It uses patient and tumor features to identify prognostic groups in whom therapy can be individualized based on predicted outcomes. Especially the survival benefit of a radical versus non-radical dose predicted by the DSS for various prognostic groups has clinical relevance, but needs to be prospectively validated. [ABSTRACT FROM AUTHOR]

To explore the association between preoperative physical performance with short- and long-term postoperative outcomes in patients undergoing lumbar spinal fusion (LSF). Retrospective cohort. University hospital. Seventy-seven patients (N=77) undergoing elective LSF were preoperatively screened on patient demographics, patient-reported outcome measures, and physical performance measures (movement control, back muscle endurance strength and extensor strength, aerobic capacity, flexibility). Not applicable. Associations between preoperative variables and inpatient functional recovery, hospital length of stay (LOS), and 1- to 2-year postoperative pain reduction were explored using random forest analyses assessing the relative influence of the variable on the outcome. Aerobic capacity was associated with fast functional recovery <4 days and prolonged functional recovery >5 days (median z scores=7.1 and 12.0). Flexibility (median z score=4.3) and back muscle endurance strength (median z score=7.8) were associated with fast functional recovery <4 days. Maximum back extensor strength was associated with prolonged functional recovery >5 days (median z score=8.6). Flexibility (median z score=5.1) and back muscle endurance strength (median z score=13.5) were associated with short LOS <5 days. Aerobic capacity (median z score=8.7) was associated with prolonged LOS >7 days. Maximum back extensor strength (median z score=3.8) was associated with 1- to 2-year postoperative pain reduction and aerobic capacity (median z score=2.8) was tentative. Physical performance measures were associated with both short- and long-term outcomes after LSF. Adding these measures to prediction models predicting outcomes after LSF may increase their accuracy. [ABSTRACT FROM AUTHOR]

Purpose Radiomics is high-throughput computerized tumour feature extraction from medical images. This has shown potential for quantifying tumour phenotype and predicting treatment response. Three major challenges impede the pace of radiomics research and clinical adoption: (i) lack of standardized methodology for radiomics analyses, (ii) lack of universal lexicon to denote features that are semantically equivalent and (iii) flat tables for radiomics output do not sufficiently capture the methodological steps that affect feature values. These barriers hamper multi-centre validation studies applying subtly different imaging protocols, pre-processing steps and extraction software. We propose an open-source Ontology-guided Radiomics Analysis Workflow (O-RAW) to address the above challenges. Methods O-RAW was developed in Python, which comprises three phases and uses two open-source component libraries (Py-rex and Pyradiomics). First, Py-rex takes standard DICOM-RT inputs (DICOM images and an RTSTRUCT file) and parses them as numpy arrays of voxel intensities and a binary mask for the volume of interest (VOI). Next, the numpy arrays are passed to Pyradiomics performing the feature extraction and returns a dictionary object to Py-rex. Lastly, Py-rex parses the dictionary as a W3C–compliant semantic web “triple store” (i.e., list of subject-predicate-object statements) with relevant semantic metalabels drawn from the Radiation Oncology Ontology and Radiomics Ontology. The output is published on a http-accessible endpoint, and can be examined via SPARQL queries. In this study, we demonstrate how DICOM-RT data can be downloaded directly from a public image repository and analysed for correlation. Results We showed that O-RAW executed efficiently on two lung cancer datasets including 21 and 32 lung cancer patients, with a median 12 and 2 VOIs per patient respectively, on a laptop running Windows 7 operating system and 8 GB RAM. The average execution time of feature extraction was 5.3  ±  0.7 s per VOI. Conclusions We successfully implemented O-RAW for radiomics analysis from radiotherapy-based images to semantic triples. With the guide of ontology, O-RAW can create findable, accessible, interoperable and reusable (FAIR) data. Its practicability and?exibility can greatly increase the development of radiomics research and finally transfer to clinical practice. We validated O-RAW on a number of publicly available image collections. [ABSTRACT FROM AUTHOR]