Dual adversarial deconfounding autoencoder for joint batch-effects removal from multi-center and multi-scanner radiomics data

Abstract Medical imaging represents the primary tool for investigating and monitoring several diseases, including cancer. The advances in quantitative image analysis have developed towards the extraction of biomarkers able to support clinical decisions. To produce robust results, multi-center studies are often set up. However, the imaging information must be denoised from confounding factors—known as batch-effect—like scanner-specific and center-specific influences. Moreover, in non-solid cancers, like lymphomas, effective biomarkers require an imaging-based representation of the disease that accounts for its multi-site spreading over the patient’s body. In this work, we address the dual-factor deconfusion problem and we propose a deconfusion algorithm to harmonize the imaging information of patients affected by Hodgkin Lymphoma in a multi-center setting. We show that the proposed model successfully denoises data from domain-specific variability (p-value < 0.001) while it coherently preserves the spatial relationship between imaging descriptions of peer lesions (p-value = 0), which is a strong prognostic biomarker for tumor heterogeneity assessment. This harmonization step allows to significantly improve the performance in prognostic models with respect to state-of-the-art methods, enabling building exhaustive patient representations and delivering more accurate analyses (p-values < 0.001 in training, p-values < 0.05 in testing). This work lays the groundwork for performing large-scale and reproducible analyses on multi-center data that are urgently needed to convey the translation of imaging-based biomarkers into the clinical practice as effective prognostic tools. The code is available on GitHub at this https://github.com/LaraCavinato/Dual-ADAE

Image Embeddings Extracted from CNNs Outperform Other Transfer Learning Approaches in Classification of Chest Radiographs

To identify the best transfer learning approach for the identification of the most frequent abnormalities on chest radiographs (CXRs), we used embeddings extracted from pretrained convolutional neural networks (CNNs). An explainable AI (XAI) model was applied to interpret black-box model predictions and assess its performance. Seven CNNs were trained on CheXpert. Three transfer learning approaches were thereafter applied to a local dataset. The classification results were ensembled using simple and entropy-weighted averaging. We applied Grad-CAM (an XAI model) to produce a saliency map. Grad-CAM maps were compared to manually extracted regions of interest, and the training time was recorded. The best transfer learning model was that which used image embeddings and random forest with simple averaging, with an average AUC of 0.856. Grad-CAM maps showed that the models focused on specific features of each CXR. CNNs pretrained on a large public dataset of medical images can be exploited as feature extractors for tasks of interest. The extracted image embeddings contain relevant information that can be used to train an additional classifier with satisfactory performance on an independent dataset, demonstrating it to be the optimal transfer learning strategy and overcoming the need for large private datasets, extensive computational resources, and long training times

Зимник-2019: Студгородок* Трансформация кампуса в Иркутске. Программа культурного, социально-экономического и пространственного развития

The article tells about the strategy of Winter University of Urban Planning and the characteristics of Winter University of Urban Planning 2019, as well as the experts, pilots and participants of the workshop. The practice-oriented approach of the workshop and the value of the campus have made the Winter University a platform for interesting discussions and innovative proposals.Рассматривается стратегия Зимнего градостроительного университета, особенности МБЗГУ 2019 года. Перечисляются эксперты и пилоты воркшопа, его участники. Практическая ориентированность воркшопа и ценность территории планирования сделали Зимник интересной территорией дискуссий и инновационных предложений

The Impact of COVID-19 on Nuclear Medicine in Europe.

The COVID-19 pandemic has profoundly changed hospital activities, including nuclear medicine (NM) practice. This review aimed to determine and describe the impact of COVID-19 on NM in Europe and critically discuss actions and strategies applied to face the pandemic. A literature search for relevant articles was performed on PubMed, covering COVID-19 studies published up until January 21, 2021. The findings were summarized according to general and specific activities within the NM departments. The pandemic strongly challenged NM departments: a reduction in the workforce has been experienced in almost every center in Europe due to personnel diagnosed with COVID-19 and other reasons related to the coronavirus. NM departments introduced procedures to limit COVID-19 transmission, including environmental and personal hygiene, social distancing, rescheduling of non-high-priority procedures, the correct use of personal protective equipment, and prompt identification of suspect COVID-19 cases. A proportion of the departments experienced a delay in radiopharmaceuticals supply or technical assistance during the pandemic. Furthermore, the pandemic resulted in a significant reduction of diagnostic and therapeutic NM procedures, as well as a reduced level of care for patients affected by diseases other than COVID-19, such as cancer or acute cardiovascular disease. Telemedicine services have been set up to maintain medical assistance for patients. COVID-19 pandemic has reshaped human work resources, patient's diagnostic and therapeutic management, operative models, radiopharmaceutical supplies, teaching, training and research of NM departments. Limits of availability of resources emerged. Nonetheless, we have to provide continuity in care, especially for fragile patients, maintaining infection control measures. Challenges that have been faced should reshape our vision and get us prepared for the future

The Role of Nuclear Cardiac Imaging in Infective Endocarditis

Purpose of Review: Infective endocarditis (IE) remains a deadly disease despite improvements in its management. Echocardiography is crucial for the diagnosis of IE; however, its value is operator-dependent and its sensitivity can decrease in the presence of valvular prosthesis. This review aims to provide an overview on the role of nuclear cardiac imaging in the diagnosis of IE. Recent Findings: Among all nuclear cardiac imaging modalities, both radiolabeled leukocyte scintigraphy and 2-deoxy-2-[fluorine-18]fluoro-D-glucose positron emission tomography/computed tomography ([18F]FDG-PET/CT) have been recently introduced in the guidelines of European Society of Cardiology (ESC) for the management of IE. The ESC guidelines included some minor criteria (mainly clinical), and two different sets of major criteria based on blood culture and imaging, respectively. The positivity of either radiolabeled leukocyte scintigraphy or [18F]FDG-PET/CT images is considered itself a major criterion to diagnose IE. However., nuclear cardiac imaging analysis may be tricky and methodological and technical aspects should be carefully considered. Summary: Available evidence supports the role of nuclear cardiac imaging in the diagnosis and management of IE. However., all practitioners who act within the â\u80\u9cEndocarditis Teamâ\u80\u9d should present a very high level of expertise

Explainable domain transfer of distant supervised cancer subtyping model via imaging-based rules extraction

Image texture analysis has for decades represented a promising opportunity for cancer assessment and disease progression evaluation, evolving in a discipline, i.e., radiomics. However, the road to a complete translation into clinical practice is still hampered by intrinsic limitations. As purely supervised classification models fail in devising robust imaging-based biomarkers for prognosis, cancer subtyping approaches would benefit from the employment of distant supervision, for instance exploiting survival/recurrence information. In this work, we assessed, tested, and validated the domain-generality of our previously proposed Distant Supervised Cancer Subtyping model on Hodgkin Lymphoma. We evaluate the model performance on two independent datasets coming from two hospitals, comparing and analyzing the results. Although successful and consistent, the comparison confirmed the instability of radiomics due to an across-center lack of reproducibility, leading to explainable results in one center and poor interpretability in the other. We thus propose a Random Forest-based Explainable Transfer Model for testing the domain-invariance of imaging biomarkers extracted from retrospective cancer subtyping. In doing so, we tested the predictive ability of cancer subtyping in a validation and perspective setting, which led to successful results and supported the domain-generality of the proposed approach. On the other hand, the extraction of decision rules enables to draw of risk factors and robust biomarkers to inform clinical decisions. This work shows the potentialities of the Distant Supervised Cancer Subtyping model to be further evaluated in larger multi-center datasets, to reliably translate radiomics into medical practice

The Development of an Intelligent Agent to Detect and Non-Invasively Characterize Lung Lesions on CT Scans: Ready for the “Real World”?

(1) Background: Once lung lesions are identified on CT scans, they must be characterized by assessing the risk of malignancy. Despite the promising performance of computer-aided systems, some limitations related to the study design and technical issues undermine these tools’ efficiency; an “intelligent agent” to detect and non-invasively characterize lung lesions on CT scans is proposed. (2) Methods: Two main modules tackled the detection of lung nodules on CT scans and the diagnosis of each nodule into benign and malignant categories. Computer-aided detection (CADe) and computer aided-diagnosis (CADx) modules relied on deep learning techniques such as Retina U-Net and the convolutional neural network; (3) Results: Tests were conducted on one publicly available dataset and two local datasets featuring CT scans acquired with different devices to reveal deep learning performances in “real-world” clinical scenarios. The CADe module reached an accuracy rate of 78%, while the CADx’s accuracy, specificity, and sensitivity stand at 80%, 73%, and 85.7%, respectively; (4) Conclusions: Two different deep learning techniques have been adapted for CADe and CADx purposes in both publicly available and private CT scan datasets. Experiments have shown adequate performance in both detection and diagnosis tasks. Nevertheless, some drawbacks still characterize the supervised learning paradigm employed in networks such as CNN and Retina U-Net in real-world clinical scenarios, with CT scans from different devices with different sensors’ fingerprints and spatial resolution. Continuous reassessment of CADe and CADx’s performance is needed during their implementation in clinical practice