Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods

Radiomics converts medical images into mineable data via a high-throughput extraction of quantitative features used for clinical decision support. However, these radiomic features are susceptible to variation across scanners, acquisition protocols, and reconstruction settings. Various investigations have assessed the reproducibility and validation of radiomic features across these discrepancies. In this narrative review, we combine systematic keyword searches with prior domain knowledge to discuss various harmonization solutions to make the radiomic features more reproducible across various scanners and protocol settings. Different harmonization solutions are discussed and divided into two main categories: image domain and feature domain. The image domain category comprises methods such as the standardization of image acquisition, post-processing of raw sensor-level image data, data augmentation techniques, and style transfer. The feature domain category consists of methods such as the identification of reproducible features and normalization techniques such as statistical normalization, intensity harmonization, ComBat and its derivatives, and normalization using deep learning. We also reflect upon the importance of deep learning solutions for addressing variability across multi-centric radiomic studies especially using generative adversarial networks (GANs), neural style transfer (NST) techniques, or a combination of both. We cover a broader range of methods especially GANs and NST methods in more detail than previous reviews

MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter Study

Simple SummaryThe prediction of pathologic complete response (pCR) to neo-adjuvant systemic therapy (NST) based on radiological assessment of pretreatment MRI exams in breast cancer patients is not possible to date. In this study, we investigated the value of pretreatment MRI-based radiomics analysis for the prediction of pCR to NST. Radiomics, clinical, and combined models were developed and validated based on MRI exams containing 320 tumors collected from two hospitals. The clinical models significantly outperformed the radiomics models for the prediction of pCR to NST and were of similar or better performance than the combined models. This indicates poor performance of the radiomics features and that in these scenarios the radiomic features did not have an added value for the clinical models developed. Due to previous and current work, we tentatively attribute the lack of significant improvement in clinical models following the addition of radiomics features to the effects of variations in acquisition and reconstruction parameters. The lack of reproducibility data meant this effect could not be analyzed. These results indicate the need for reproducibility studies to preselect reproducible features in order to properly assess the potential of radiomics.This retrospective study investigated the value of pretreatment contrast-enhanced Magnetic Resonance Imaging (MRI)-based radiomics for the prediction of pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients. A total of 292 breast cancer patients, with 320 tumors, who were treated with neo-adjuvant systemic therapy and underwent a pretreatment MRI exam were enrolled. As the data were collected in two different hospitals with five different MRI scanners and varying acquisition protocols, three different strategies to split training and validation datasets were used. Radiomics, clinical, and combined models were developed using random forest classifiers in each strategy. The analysis of radiomics features had no added value in predicting pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients compared with the clinical models, nor did the combined models perform significantly better than the clinical models. Further, the radiomics features selected for the models and their performance differed with and within the different strategies. Due to previous and current work, we tentatively attribute the lack of improvement in clinical models following the addition of radiomics to the effects of variations in acquisition and reconstruction parameters. The lack of reproducibility data (i.e., test-retest or similar) meant that this effect could not be analyzed. These results indicate the need for reproducibility studies to preselect reproducible features in order to properly assess the potential of radiomics

Elite opinion and foreign policy in post-communist Russia

Russian elite opinion on matters of foreign policy may be classified as ‘Liberal Westerniser’, ‘Pragmatic Nationalist’ and ‘Fundamentalist Nationalist’, terms that reflect longstanding debates about the country’s relationship with the outside world. An analysis of press statements and election manifestoes together with a programme of elite interviews between 2004 and 2006 suggests a clustering of opinion on a series of strategic issues. Liberal Westernisers seek the closest possible relationship with Europe, and favour eventual membership of the EU and NATO. Pragmatic Nationalists are more inclined to favour practical co-operation, and do not assume an identity of values or interests with the Western countries. Fundamentalist Nationalists place more emphasis on the other former Soviet republics, and on Asia as much as Europe, and see the West as a threat to Russian values as well as to its state interests. Each of these positions, in turn, draws on an identifiable set of domestic constituencies: Liberal Westernisers on the promarket political parties, Pragmatic Nationalists on the presidential administration and defence and security ministries, and Fundamentalist Nationalists on the Orthodox Church and Communists

A Prospectively Validated Prognostic Model for Patients with Locally Advanced Squamous Cell Carcinoma of the Head and Neck Based on Radiomics of Computed Tomography Images

Background: Locoregionally advanced head and neck squamous cell carcinoma (HNSCC) patients have high relapse and mortality rates. Imaging-based decision support may improve out-comes by optimising personalised treatment, and support patient risk stratification. We propose a multifactorial prognostic model including radiomics features to improve risk stratification for advanced HNSCC, compared to TNM eighth edition, the gold standard. Patient and methods: Data of 666 retrospective-and 143 prospective-stage III-IVA/B HNSCC patients were collected. A multivar-iable Cox proportional-hazards model was trained to predict overall survival (OS) using diagnostic CT-based radiomics features extracted from the primary tumour. Separate analyses were performed using TNM8, tumour volume, clinical and biological variables, and combinations thereof with radi-omics features. Patient risk stratification in three groups was assessed through Kaplan–Meier (KM) curves. A log-rank test was performed for significance (p-value < 0.05). The prognostic accuracy was reported through the concordance index (CI). Results: A model combining an 11-feature radiomics signature, clinical and biological variables, TNM8, and volume could significantly stratify the validation cohort into three risk groups (p < 0∙01, CI of 0.79 as validation). Conclusion: A combination of radiomics features with other predictors can predict OS very accurately for advanced HNSCC patients and improves on the current gold standard of TNM8

Beyond automatic medical image segmentation - the spectrum between fully manual and fully automatic delineation

Semi-automatic and fully automatic contouring tools have emerged as an alternative to fully manual segmentation to reduce time spent contouring and to increase contour quality and consistency. Particularly, fully automatic segmentation has seen exceptional improvements through the use of deep learning in recent years. These fully automatic methods may not require user interactions, but the resulting contours are often not suitable to be used in clinical practice without a review by the clinician. Furthermore, they need large amounts of labeled data to be available for training. This review presents alternatives to manual or fully automatic segmentation methods along the spectrum of variable user interactivity and data availability. The challenge lies to determine how much user interaction is necessary and how this user interaction can be used most effectively. While deep learning is already widely used for fully automatic tools, interactive methods are just at the starting point to be transformed by it. Interaction between clinician and machine, via artificial intelligence, can go both ways and this review will present the avenues that are being pursued to improve medical image segmentation

DATA PROCESSING CENTER FOR RADIOASTRON PROJECT HARDWARE OPTIMIZATION

The RadioAstron is an international space VLBI project led by the Astro Space Center of Lebedev Physical Institute inMoscow,Russia. The payload - Space Radio Telescope, is based on spacecraft Spektr-R, that have been designed by the Lavochkin Association. The Spektr-R space craft was successfully launched 18 th July 2011.In this article we want to summarize Radioastron data processing center work during 2 last years. Tasks facing of the scientific data processing center are organization of service information exchange, collection of scientific ob­servations, storage of scientific observations, processing of scientific observation data Radioastron project

Calculating the thermal interaction of different structures with permanently frozen grounds at the base

На сегодняшний день для выполнения теплотехнических расчетов по определению теплосилового взаимодействия сооружений с многолетнемерзлыми грунтами широко применяется численное моделирование. Известным преимуществом численного моделирования является возможность учитывать ярко выраженную неоднородность грунта по глубине, сложный тепловой режим работы сооружений, а также сочетание различных условий теплообмена на поверхности. Однако недостатком такой сложной численной модели является в первую очередь вопрос о корректности ее построения и результатов моделирования. В данной статье рассмотрена проблема определения достоверности численной геокриологической модели. Ставятся задачи по определению критериев достоверности математической модели, а также по разработке методики, применение которой позволяет определять достоверность такой модели. Решение данных задач подробным образом описано на примере, авторами введено понятие пространственно-временной характеристики геокриологических условий, предложен комплекс критериев достоверности численной геокриологической модели, а также разработана и предложена методика, применение которой позволяет говорить о достоверности численной геокриологической модели.Numerical modeling is widely applied for performance of heattechnical calculations for definition of heatpower interaction of constructions with permafrost soil. The known advantage of numerical modeling is an opportunity to consider pronounced inhomogeneity of soil in depth, a difficult thermal operating mode of constructions and a combination of various conditions of heat exchange on a surface. However, a lack of such difficult numerical model is first the question of correctness of its construction and results of modeling. This article studies the problem of determination of reliability of numerical geocryologic model. Tasks of determination of criteria of reliability of mathematical model and of development of a technique which use allows defining reliability of such model are set. The authors present the solution of these tasks, providing a concept of space-time characteristic of geocryologic conditions, the complex of criteria of reliability of numerical geocryologic model. They have also developed the technique and its application, which speaks for the reliability of numerical geocryologic model

Dedicated Axillary MRI-Based Radiomics Analysis for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer

Simple SummaryThe presence of axillary lymph node metastases in breast cancer patients is an essential factor in axillary surgery and possible additional treatment. This study aimed to investigate the potential of dedicated axillary MRI-based radiomics analysis for the prediction of axillary lymph node metastases. Dedicated axillary MRI examinations provide a very specific and complete field of view of the axilla. Accurate preoperative prediction of axillary lymph node metastases in breast cancer patients using radiomics analysis can aid in clinical decision-making for the type of treatment.Radiomics features may contribute to increased diagnostic performance of MRI in the prediction of axillary lymph node metastasis. The objective of the study was to predict preoperative axillary lymph node metastasis in breast cancer using clinical models and radiomics models based on T2-weighted (T2W) dedicated axillary MRI features with node-by-node analysis. From August 2012 until October 2014, all women who had undergone dedicated axillary 3.0T T2W MRI, followed by axillary surgery, were retrospectively identified, and available clinical data were collected. All axillary lymph nodes were manually delineated on the T2W MR images, and quantitative radiomics features were extracted from the delineated regions. Data were partitioned patient-wise to train 100 models using different splits for the training and validation cohorts to account for multiple lymph nodes per patient and class imbalance. Features were selected in the training cohorts using recursive feature elimination with repeated 5-fold cross-validation, followed by the development of random forest models. The performance of the models was assessed using the area under the curve (AUC). A total of 75 women (median age, 61 years; interquartile range, 51-68 years) with 511 axillary lymph nodes were included. On final pathology, 36 (7%) of the lymph nodes had metastasis. A total of 105 original radiomics features were extracted from the T2W MR images. Each cohort split resulted in a different number of lymph nodes in the training cohorts and a different set of selected features. Performance of the 100 clinical and radiomics models showed a wide range of AUC values between 0.41-0.74 and 0.48-0.89 in the training cohorts, respectively, and between 0.30-0.98 and 0.37-0.99 in the validation cohorts, respectively. With these results, it was not possible to obtain a final prediction model. Clinical characteristics and dedicated axillary MRI-based radiomics with node-by-node analysis did not contribute to the prediction of axillary lymph node metastasis in breast cancer based on data where variations in acquisition and reconstruction parameters were not addressed