Post-mortem CT radiomics for the prediction of time since death.

OBJECTIVES: Post-mortem interval (PMI) estimation has long been relying on sequential post-mortem changes on the body as a function of extrinsic, intrinsic, and environmental factors. Such factors are difficult to account for in complicated death scenes; thus, PMI estimation can be compromised. Herein, we aimed to evaluate the use of post-mortem CT (PMCT) radiomics for the differentiation between early and late PMI. METHODS: Consecutive whole-body PMCT examinations performed between 2016 and 2021 were retrospectively included (n = 120), excluding corpses without an accurately reported PMI (n = 23). Radiomics data were extracted from liver and pancreas tissue and randomly split into training and validation sets (70:30%). Following data preprocessing, significant features were selected (Boruta selection) and three XGBoost classifiers were built (liver, pancreas, combined) to differentiate between early (< 12 h) and late (> 12 h) PMI. Classifier performance was assessed with receiver operating characteristics (ROC) curves and areas under the curves (AUC), which were compared by bootstrapping. RESULTS: A total of 97 PMCTs were included, representing individuals (23 females and 74 males) with a mean age of 47.1 ± 23.38 years. The combined model achieved the highest AUC reaching 75% (95%CI 58.4-91.6%) (p = 0.03 compared to liver and p = 0.18 compared to pancreas). The liver-based and pancreas-based XGBoost models achieved AUCs of 53.6% (95%CI 34.8-72.3%) and 64.3% (95%CI 46.7-81.9%) respectively (p > 0.05 for the comparison between liver- and pancreas-based models). CONCLUSION: The use of radiomics analysis on PMCT examinations differentiated early from late PMI, unveiling a novel image-based method with important repercussions in forensic casework. CLINICAL RELEVANCE STATEMENT: This paper introduces the employment of radiomics in forensic diagnosis by presenting an effective automated alternative method of estimating post-mortem interval from targeted tissues, thus paving the way for improvement in speed and quality of forensic investigations. KEY POINTS: • A combined liver-pancreas radiomics model differentiated early from late post-mortem intervals (using a 12-h threshold) with an area under the curve of 75% (95%CI 58.4-91.6%). • XGBoost models based on liver-only or pancreas-only radiomics demonstrated inferior performance to the combined model in predicting the post-mortem interval.

Developing a Radiomics Atlas Dataset of normal Abdominal and Pelvic computed Tomography (RADAPT).

Atlases of normal genomics, transcriptomics, proteomics, and metabolomics have been published in an attempt to understand the biological phenotype in health and disease and to set the basis of comprehensive comparative omics studies. No such atlas exists for radiomics data. The purpose of this study was to systematically create a radiomics dataset of normal abdominal and pelvic radiomics that can be used for model development and validation. Young adults without any previously known disease, aged > 17 and ≤ 36 years old, were retrospectively included. All patients had undergone CT scanning for emergency indications. In case abnormal findings were identified, the relevant anatomical structures were excluded. Deep learning was used to automatically segment the majority of visible anatomical structures with the TotalSegmentator model as applied in 3DSlicer. Radiomics features including first order, texture, wavelet, and Laplacian of Gaussian transformed features were extracted with PyRadiomics. A Github repository was created to host the resulting dataset. Radiomics data were extracted from a total of 531 patients with a mean age of 26.8 ± 5.19 years, including 250 female and 281 male patients. A maximum of 53 anatomical structures were segmented and used for subsequent radiomics data extraction. Radiomics features were derived from a total of 526 non-contrast and 400 contrast-enhanced (portal venous) series. The dataset is publicly available for model development and validation purposes.

Radiomics Analysis for Multiple Myeloma: A Systematic Review with Radiomics Quality Scoring.

Multiple myeloma (MM) is one of the most common hematological malignancies affecting the bone marrow. Radiomics analysis has been employed in the literature in an attempt to evaluate the bone marrow of MM patients. This manuscript aimed to systematically review radiomics research on MM while employing a radiomics quality score (RQS) to accurately assess research quality in the field. A systematic search was performed on Web of Science, PubMed, and Scopus. The selected manuscripts were evaluated (data extraction and RQS scoring) by three independent readers (R1, R2, and R3) with experience in radiomics analysis. A total of 23 studies with 2682 patients were included, and the median RQS was 10 for R1 (IQR 5.5-12) and R3 (IQR 8.3-12) and 11 (IQR 7.5-12.5) for R2. RQS was not significantly correlated with any of the assessed bibliometric data (impact factor, quartile, year of publication, and imaging modality) (p > 0.05). Our results demonstrated the low quality of published radiomics research in MM, similarly to other fields of radiomics research, highlighting the need to tighten publication standards.

Frailty Status Affects the Decision for Long-Term Anticoagulation Therapy in Elderly Patients with Atrial Fibrillation.

BACKGROUND: Elderly patients are underrepresented in the studies concerning anticoagulation therapy (AT) in atrial fibrillation (AF), while patients' frailty status is lacking in most of the studies. OBJECTIVE: Our objective was to evaluate AT in AF elderly patients and study the effect of patients' frailty status on their long-term AT. METHODS: We conducted an observational prospective study that enrolled consecutive AF patients (≥ 75 years) who were hospitalized in the Department of Internal Medicine of the University Hospital of Heraklion, Crete, Greece from 1 June 2015 to 1 June 2016. We recorded the AT on admission and at discharge, all-cause mortality, and hospital readmission in a follow-up period of 1 year after hospital discharge. Frailty status was assessed by pre-established scores. RESULTS: One hundred and four consecutive patients (49% male; median age 87 years) were enrolled, 78 (78.8%) of whom received AT at discharge. Patients who did not receive AT at discharge had a higher HEMORR2HAGES (Hepatic or renal disease, Ethanol abuse, Malignancy, Older age, Reduced platelet count or function, Re-bleeding, Hypertension, Anemia, Genetic factors, Excessive fall risk and Stroke) score (5.5 ± 1.15 vs. 4.79 ± 1.68; p = 0.032), a lower Katz score (2.48 ± 2.23 vs. 4.08 ± 2.25; p = 0.006), and a higher Clinical Frailty Scale score (7 ± 1.95 vs. 5.57 ± 2.05; p = 0.006). Sixty-five patients (62.5%) were readmitted to a hospital during the follow-up period. In-hospital death occurred in five patients (4.8%) and 57 patients (57.6%) died within the follow-up period. CONCLUSION: A high percentage of the elderly AF patients did not receive AT, even at discharge. Patients who did not receive AT at discharge had higher bleeding and frailty scores. In the 1-year follow-up period after hospital discharge, high all-cause mortality and a high number of hospital readmissions were recorded.