Background: Histopathology of first-trimester abortion products may be useful in document an intrauterine pregnancy, identifying an important pathology affecting the mother or the embryo and diagnosing conditions that are likely to recur in future pregnancies or that explain the adverse fetal outcome. Relevant information provided by histology is essential to determine the cause and to guide the patients with early pregnancy failure.
Aims: Histopathological classification proposal in first-trimester miscarriage.
Methods: Published pathological criteria in first-trimester abortion specimens were collected, standardized and focused into a comprehensive diagnosis. The idea was to create a comprehensive classification related to major pathophysiological processes. Thus, the histological criteria were grouped into 7 categories: i. Changes suggesting aneuploidy (CSA) or metabolic storage disease; ii. Embryo anomaly (EA); iii. Multifactorial (MF) causes; iv. Maternal causes (MC); v. Gestational trophoblastic disease, such as hydatidiform mole (HM) and non neoplastic lesions and neoplasms; vi. Ectopic pregnancy; vii. Other. So, a 6-years retrospective study of first-trimester spontaneous miscarriage were reviewed. Two groups were created: i. Study group include specimens with pathological diagnosis; ii. Control group incorporate specimens with pathological diagnosis and additional genetic study in order to validate pathological criteria.
Results: Pathological criteria concordance between inter-observers was generally good, with an excellent correlation in EA and HM categories. Despite greater inter-observer disagreement in the CSA and MC categories the correlation with the genetic results was very positive.
Conclusion: A standardized, reproducible and biologically comprehensive histopathological classification may improve fetal follow-up and couple's management.
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http://dx.doi.org/10.1016/j.heliyon.2021.e06359 | DOI Listing |
Acad Radiol
December 2024
Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104 (D.A.T.). Electronic address:
Rationale And Objectives: Prostate cancer (PCa) is the second most common cancer among men and a leading cause of cancer-related mortalities. Radiomics has shown promising performances in the classification of PCa grade group (GG) in several studies. Here, we aimed to systematically review and meta-analyze the performance of radiomics in predicting GG in PCa.
View Article and Find Full Text PDFFront Oncol
December 2024
Newcastle Magnetic Resonance Centre, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom.
Introduction: Ductal carcinoma (DCIS) accounts for 25% of newly diagnosed breast cancer cases with only 14%-53% developing into invasive ductal carcinoma (IDC), but currently overtreated due to inadequate accuracy of mammography. Subtypes of calcification, discernible from histology, has been suggested to have prognostic value in DCIS, while the lipid composition of saturated and unsaturated fatty acids may be altered in synthesis with potential sensitivity to the difference between DCIS and IDC. We therefore set out to examine calcification using ultra short echo time (UTE) MRI and lipid composition using chemical shift-encoded imaging (CSEI), as markers for histological calcification classification, in the initial step towards application.
View Article and Find Full Text PDFJ Med Signals Sens
December 2024
Department of Radiation Sciences, School of Allied Medicine, Iran University of Medical Sciences, Tehran, Iran.
Purpose: This study aimed to investigate the performance of multiparametric magnetic resonance imaging (mpMRI) radiomic feature-based machine learning (ML) models in classifying the Gleason grade group (GG) of prostate cancer.
Methods: In this retrospective study, a total of 203 patients with histopathologically confirmed prostate cancer who underwent mpMRI before prostate biopsy were included. After manual segmentation, radiomic features (RFs) were extracted from T2-weighted, apparent diffusion coefficient, and high b-value diffusion-weighted magnetic resonance imaging (DWMRI).
In Vivo
December 2024
Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany;
Background/aim: The recently published Node-Reporting and Data System (Node-RADS) can aid the characterization of lymph nodes in cross-sectional imaging. This study investigated the Node-RADS system in computed tomography (CT) to characterize lymph nodes in esophageal cancer.
Patients And Methods: Overall, 126 patients (15 female, 11.
Sci Rep
December 2024
Department of Radiology and Imaging Sciences, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai, 600 116, India.
Distinguishing between primary adenocarcinoma (AC) and squamous cell carcinoma (SCC) within non-small cell lung cancer (NSCLC) tumours holds significant management implications. We assessed the performance of radiomics-based models in distinguishing primary there is from SCC presenting as lung nodules on Computed Tomography (CT) scans. We studied individuals with histopathologically proven adenocarcinoma or SCC type NSCLC tumours, detected as lung nodules on Chest CT.
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