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Radiological follow-up of oncology patients requires the detection of lesions and the quantitative analysis of lesion changes in longitudinal imaging studies of patients, which is time-consuming and requires expertise. We present here a new method and workflow for the analysis and review of lesions and volumetric lesion changes in longitudinal scans of a patient. The generic graph-based method consists of lesion matching, classification of changes in individual lesions, and detection of patterns of lesion changes computed from the properties of the graph and its connected components. The workflow guides clinicians in the detection of missed lesions and wrongly identified lesions in manual and computed lesion annotations using the analysis of lesion changes. It serves as a heuristic method for the automatic revision of ground truth lesion annotations in longitudinal scans. The methods were evaluated on longitudinal studies of patients with three or more examinations of metastatic lesions in the lung (19 patients, 83 CT scans, 1178 lesions), the liver (18 patients, 77 CECT scans, 800 lesions) and the brain (30 patients, 102 T1W-Gad MRI scans, 317 lesions) with ground-truth lesion annotations. Lesion matching yielded a precision of 0.92-1.0 and recall of 0.91-0.99. The classification of changes in individual lesions yielded an accuracy of 0.87-0.97. The classification of patterns of lesion changes yielded an accuracy of 0.80-0.94. The lesion detection review workflow applied to manual and computed lesion annotations yielded 120 and 55 missed lesions and 20 and 164 wrongly identified lesions for all longitudinal studies of patients, respectively. The automatic analysis of lesion changes and review of lesion detection in longitudinal studies of oncological patients helps detect missed lesions and wrongly identified lesions. This method may help improve the accuracy of radiological interpretation and the disease status evaluation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Purpose: Radiological follow-up of oncology patients requires the quantitative analysis of lesion changes in longitudinal imaging studies, which is time-consuming, requires expertise, and is subject to variability. This paper presents a comprehensive graph-based method for the automatic detection and classification of lesion changes in current and prior CT scans., Methods: The inputs are the current and prior CT scans and their organ and lesion segmentations. Classification of lesion changes is formalized as bipartite graph matching where lesion pairings are computed by adaptive overlap-based lesion matching. Six types of lesion changes are computed by connected components analysis. The method was evaluated on 208 pairs of lung and liver CT scans from 57 patients with 4600 lesions, 1713 lesion matchings and 2887 lesion changes. Ground-truth lesion segmentations, lesion matchings and lesion changes were created by an expert radiologist., Results: Our method yields a lesion matching rate accuracy of 99.7% (394/395) and 95.0% (1252/1318) for the lung and liver datasets. Precision and recall are > 0.99 and 0.94 and 0.95 (respectively) for the detection of lesion changes. The analysis of lesion changes helped the radiologist detect 48 missed lesions and 8 spurious lesions in the input ground-truth lesion datasets., Conclusion: The classification of lesion classification provides the clinician with a readily accessible and intuitive identification and classification of the lesion changes and their patterns in support of clinical decision making. Comprehensive automatic computer-aided lesion matching and analysis of lesion changes may improve quantitative follow-up and evaluation of disease status, assessment of treatment efficacy and response to therapy., (© 2023. CARS.)

Resolving the structural variability of proteins is often key to understanding the structure-function relationship of those macromolecular machines. Single particle analysis using Cryogenic electron microscopy (CryoEM), combined with machine learning algorithms, provides a way to reveal the dynamics within the protein system from noisy micrographs. Here, we introduce an improved computational method that uses Gaussian mixture models for protein structure representation and deep neural networks for conformation space embedding. By integrating information from molecular models into the heterogeneity analysis, we can analyze continuous protein conformational changes using structural information at the frequency of 1/3 Å -1 , and present the results in a more interpretable form., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The new developments in Cryo-EM Single Particle Analysis are helping us to understand how the macromolecular structure and function meet to drive biological processes. By capturing many states at the particle level, it is possible to address how macromolecules explore different conformations, information that is classically extracted through 3D classification. However, the limitations of classical approaches prevent us from fully understanding the complete conformational landscape due to the reduced number of discrete states accurately reconstructed. To characterize the whole structural spectrum of a macromolecule, we propose an extension of our Zernike3D approach, able to extract per-image continuous flexibility information directly from a particle dataset. Also, our method can be seamlessly applied to images, maps or atomic models, opening integrative possibilities. Furthermore, we introduce the ZART reconstruction algorithm, which considers the Zernike3D deformation fields to revert particle conformational changes during the reconstruction process, thus minimizing the blurring induced by molecular motions., (© 2023. The Author(s).)

Background: Read alignment is a computational bottleneck in some sequencing projects. Most of the existing software packages for read alignment are based on two algorithmic approaches: prefix-trees and hash-tables. We propose a new approach to read alignment using random permutations of strings., Results: We present a prototype implementation and experiments performed with simulated and real reads of human DNA. Our experiments indicate that this permutations-based prototype is several times faster than comparable programs for fast read alignment and that it aligns more reads correctly., Conclusions: This approach may lead to improved speed, sensitivity, and accuracy in read alignment. The algorithm can also be used for specialized alignment applications and it can be extended to other related problems, such as assembly.More information: http://alignment.commons.yale.edu.

Local use of practice guidelines requires paying close attention to the concerns of the patient within the framework of society, to the professional and educational needs of the provider, and to the realities of cost. One Veterans Affairs facility took the challenge of balancing these factors and developed their own algorithms for three cardiovascular disorders.

The Laplace Transform is frequently encountered in mathematics, physics, engineering, and other fields. However, the spectral properties of the Laplace Transform tend to complicate its numerical treatment; therefore, the closely related "Truncated" Laplace Transforms are often used in applications. We have constructed efficient algorithms for the evaluation of the left singular functions and singular values of the Truncated Laplace Transform. Together with the previously introduced algorithms for the evaluation of the right singular functions, these algorithms provide the singular value decomposition of the Truncated Laplace Transform. The resulting algorithms are applicable to all environments likely to be encountered in applications, including the evaluation of singular functions corresponding to extremely small singular values (e.g., 10-1000). [ABSTRACT FROM AUTHOR]

The Laplace Transform is frequently encountered in mathematics, physics, engineering, and other fields. However, the spectral properties of the Laplace Transform tend to complicate its numerical treatment; therefore, the closely related "Truncated" Laplace Transforms are often used in applications. We have constructed efficient algorithms for the evaluation of the Singular Value Decomposition of Truncated Laplace Transforms; in the current paper, we introduce algorithms for the evaluation of the right singular functions and singular values of Truncated Laplace Transforms. Algorithms for the computation of the left singular functions will be introduced separately in an upcoming paper. The resulting algorithms are applicable to all environments likely to be encountered in applications, including the evaluation of singular functions corresponding to extremely small singular values (e.g., 10-1000). [ABSTRACT FROM AUTHOR]