Exploring radiomic features of lateral cerebral ventricles in postmortem CT for postmortem interval estimation.

The aim of this study is to investigate the potential of radiomic features extracted from postmortem computed tomography (PMCT) scans of the lateral cerebral ventricles (LCVs) to provide information on the time since death, or postmortem interval (PMI), a critical aspect of forensic medicine. Periodic PMCT scans, referred to as "sequential scans", were obtained from twelve corpses with known times of death, ranging from 5.5 to 273 h postmortem.

DYNAMITE: Integrating Archetypal Analysis and Process Mining for Interpretable Disease Progression Modelling.

DYNAMITE, an acronym for DYNamic Archetypal analysis for MIning disease TrajEctories, is a new methodology developed specifically to model disease progression by exploiting information available in longitudinal clinical datasets. First, archetypal analysis is applied to data organised in matrix form, with the aim of finding extreme and representative disease states (archetypes) linked to the original data through convex coefficients. Then, each original observation is associated with a single archetype based on their similarity; finally, an event log is created encoding the progression of disease states for each patient in terms of archetype states.

Securing Reproducibility and Accountability in Distributed Healthcare Analytics: A Framework Based on Blockchain and Cryptography.

The results and details of the clinical studies and research must be securely stored to ensure reliability, accountability, and prevent malicious misuse. To accomplish this, a secure method for storing metadata and study results is crucial. Also, a mechanism to ensure accountability for both data owners and researchers is needed.

The psychological impact of diagnosis of sinonasal, nasopharyngeal, and laryngeal cancers: a systematic review.

Objective: High risk of degraded quality of life and psychological distress is observed in patients diagnosed with sinonasal, nasopharyngeal, and laryngeal cancers, which could be caused by exposure to carcinogens in workplaces. This review aimed to investigate the psychological impact of diagnosis associated with the possible occupational etiology of such neoplasms and to explore the instruments that evaluate the quality of life (QoL), anxiety, and depression in these patients.

Methods: Studies were considered for the review only if they described aspects of the psychological impact of the diagnosis of sinonasal, nasopharyngeal, and laryngeal cancers and reported results distinguished by the tumor site.

GEN-RWD Sandbox: bridging the gap between hospital data privacy and external research insights with distributed analytics.

Background: Artificial intelligence (AI) has become a pivotal tool in advancing contemporary personalised medicine, with the goal of tailoring treatments to individual patient conditions. This has heightened the demand for access to diverse data from clinical practice and daily life for research, posing challenges due to the sensitive nature of medical information, including genetics and health conditions. Regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.

Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?

Artificial intelligence (AI) is emerging as a discipline capable of providing significant added value in Medicine, in particular in radiomic, imaging analysis, big dataset analysis, and also for generating virtual cohort of patients. However, in coping with chronic myeloid leukemia (CML), considered an easily managed malignancy after the introduction of TKIs which strongly improved the life expectancy of patients, AI is still in its infancy. Noteworthy, the findings of initial trials are intriguing and encouraging, both in terms of performance and adaptability to different contexts in which AI can be applied.

Immunohistochemical expression of HMGB1 and related proteins in the skin as a possible tool for determining post-mortem interval: a preclinical study.

Determining the post-mortem interval (PMI) is one of forensic pathology's primary objectives and one of its most challenging tasks. Numerous studies have demonstrated the accuracy of histomorphology and immunohistochemical investigations in determining the time of death. Nevertheless, the skin, a robust and easy-to-remove tissue, has only been partially analyzed so far.

Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review.

Background: In the last years growing evidences on the role of radiomics and machine learning (ML) applied to different nuclear medicine imaging modalities for the assessment of thyroid diseases are starting to emerge. The aim of this systematic review was therefore to analyze the diagnostic performances of these technologies in this setting.

Methods: A wide literature search of the PubMed/MEDLINE, Scopus and Web of Science databases was made in order to find relevant published articles about the role of radiomics or ML on nuclear medicine imaging for the evaluation of different thyroid diseases.

Baseline F-FDG PET/CT Radiomics in Classical Hodgkin's Lymphoma: The Predictive Role of the Largest and the Hottest Lesions.

This study investigated the predictive role of baseline F-FDG PET/CT (bPET/CT) radiomics from two distinct target lesions in patients with classical Hodgkin's lymphoma (cHL). cHL patients examined with bPET/CT and interim PET/CT between 2010 and 2019 were retrospectively included. Two bPET/CT target lesions were selected for radiomic feature extraction: Lesion_A, with the largest axial diameter, and Lesion_B, with the highest SUV.

Semiautomated Pipeline to Quantify Tumor Evolution From Real-World Positron Emission Tomography/Computed Tomography Imaging.

Purpose: A semiautomated pipeline for the collection and curation of free-text and imaging real-world data (RWD) was developed to quantify cancer treatment outcomes in large-scale retrospective real-world studies. The objectives of this article are to illustrate the challenges of RWD extraction, to demonstrate approaches for quality assurance, and to showcase the potential of RWD for precision oncology.

Methods: We collected data from patients with advanced melanoma receiving immune checkpoint inhibitors at the Lausanne University Hospital.

Interactive process mining of cancer treatment sequences with melanoma real-world data.

The growing availability of clinical real-world data (RWD) represents a formidable opportunity to complement evidence from randomized clinical trials and observe how oncological treatments perform in real-life conditions. In particular, RWD can provide insights on questions for which no clinical trials exist, such as comparing outcomes from different sequences of treatments. To this end, process mining is a particularly suitable methodology for analyzing different treatment paths and their associated outcomes.

Leveraging process mining for modeling progression trajectories in amyotrophic lateral sclerosis.

Background: Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease whose spreading and progression mechanisms are still unclear. The ability to predict ALS prognosis would improve the patients' quality of life and support clinicians in planning treatments. In this paper, we investigate ALS evolution trajectories using Process Mining (PM) techniques enriched to both easily mine processes and automatically reveal how the pathways differentiate according to patients' characteristics.

Radiomics to predict immunotherapy efficacy in advanced renal cell carcinoma: A retrospective study.

Immunotherapy has become a cornerstone for the treatment of renal cell carcinoma. Nevertheless, some patients are resistant to immune checkpoint inhibitors. The possibility to identify patients who cannot benefit from immunotherapy is a relevant clinical challenge.

Role of Radiomics Features and Machine Learning for the Histological Classification of Stage I and Stage II NSCLC at [F]FDG PET/CT: A Comparison between Two PET/CT Scanners.

The aim of this study was to compare two different PET/CT tomographs for the evaluation of the role of radiomics features (RaF) and machine learning (ML) in the prediction of the histological classification of stage I and II non-small-cell lung cancer (NSCLC) at baseline [F]FDG PET/CT. A total of 227 patients were retrospectively included and, after volumetric segmentation, RaF were extracted. All of the features were tested for significant differences between the two scanners and considering both the scanners together, and their performances in predicting the histology of NSCLC were analyzed by testing of different ML approaches: Logistic Regressor (LR), k-Nearest Neighbors (kNN), Decision Tree (DT) and Random Forest (RF).

A differential process mining analysis of COVID-19 management for cancer patients.

During the acute phase of the COVID-19 pandemic, hospitals faced a challenge to manage patients, especially those with other comorbidities and medical needs, such as cancer patients. Here, we use Process Mining to analyze real-world therapeutic pathways in a cohort of 1182 cancer patients of the Lausanne University Hospital following COVID-19 infection. The algorithm builds trees representing sequences of coarse-grained events such as Home, Hospitalization, Intensive Care and Death.

Unattended versus Attended Blood Pressure Measurement: Relationship with Retinal Microcirculation.

Though the relationship between both “attended” and “unattended” BP and several forms of target organ damage have been evaluated, data on retinal arteriolar alterations are lacking. The aim of our study was to evaluate the relationship between “attended” or “unattended” BP values and retinal arteriolar changes in consecutive individuals undergoing a clinical evaluation and assessment of retinal fundus at an ESH Excellence Centre. An oscillometric device programmed to perform 3 BP measurements, at 1 min intervals and after 5 min of rest was used on all individuals to measure BP with the patient alone in the room (“unattended”) or in the presence of the physician (“attended”) in the same day in a random order.

F-Fluoroethylcholine PET/CT Radiomic Analysis for Newly Diagnosed Prostate Cancer Patients: A Monocentric Study.

Aim: The aim of this study is to assess whether there are some correlations between radiomics and baseline clinical-biological data of prostate cancer (PC) patients using Fluorine-18 Fluoroethylcholine (F-FECh) PET/CT.

Methods: Digital rectal examination results (DRE), Prostate-Specific Antigen (PSA) serum levels, and bioptical-Gleason Score (GS) were retrospectively collected in newly diagnosed PC patients and considered as outcomes of PC. Thereafter, Volumes of interest (VOI) encompassing the prostate of each patient were drawn to extract conventional and radiomic PET features.

Could 18-FDG PET-CT Radiomic Features Predict the Locoregional Progression-Free Survival in Inoperable or Unresectable Oesophageal Cancer?

Background: We evaluated the value of pre-treatment positron-emission tomography−computed tomography (PET-CT)-based radiomic features in predicting the locoregional progression-free survival (LR-PFS) of patients with inoperable or unresectable oesophageal cancer. Material and Methods: Forty-six patients were included and 230 radiomic parameters were extracted. After a principal component analysis (PCA), we identified the more robust radiomic parameters, and we used them to develop a heatmap.

Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers.

The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort.

Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers.

Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging.

A Process Mining Pipeline to Characterize COVID-19 Patients' Trajectories and Identify Relevant Temporal Phenotypes From EHR Data.

The impact of the COVID-19 pandemic involved the disruption of the processes of care and the need for immediately effective re-organizational procedures. In the context of digital health, it is of paramount importance to determine how a specific patients' population reflects into the healthcare dynamics of the hospital, to investigate how patients' sub-group/strata respond to the different care processes, in order to generate novel hypotheses regarding the most effective healthcare strategies. We present an analysis pipeline based on the heterogeneous collected data aimed at identifying the most frequent healthcare processes patterns, jointly analyzing them with demographic and physiological disease trajectories, and stratify the observed cohort on the basis of the mined patterns.

Influence of glucose on swarming and quorum sensing of Dickeya solani.

Dickeya solani is a pathogen most frequently responsible for infecting potato plants in Europe. As in the case of most plant pathogens, its ability to colonize and invade the host depends on chemotaxis and motility. The coordinated movement of Dickeya over solid surfaces is governed by a quorum sensing mechanism.

Comparison between Two Different Scanners for the Evaluation of the Role of F-FDG PET/CT Semiquantitative Parameters and Radiomics Features in the Prediction of Final Diagnosis of Thyroid Incidentalomas.

The aim of this study was to compare two different tomographs for the evaluation of the role of semiquantitative PET/CT parameters and radiomics features (RF) in the prediction of thyroid incidentalomas (TIs) at F-FDG imaging. A total of 221 patients with the presence of TIs were retrospectively included. After volumetric segmentation of each TI, semiquantitative parameters and RF were extracted.