Appendiceal intussusception due to endometriosis presenting as acute right lower quadrant pain.

Appendiceal intussusception is a rare condition characterized by the telescoping or invagination of a portion or the entire appendix into the caecum or within the appendix itself. Diagnosing appendiceal intussusception can be challenging due to its rarity, non-specific symptoms, and lack of awareness among physicians. We present a case report of appendiceal intussusception caused by endometriosis presenting with recurrent abdominal pain in a young female that was initially missed on CT scan and laparoscopy and eventually diagnosed on CT enterography.

Multisystem Imaging Manifestations of Kidney Failure.

Kidney failure (KF) refers to a progressive decline in glomerular filtration rate to below 15 ml/min per 1.73 m, necessitating renal replacement therapy with dialysis or renal transplant. The hemodynamic and metabolic alterations in KF combined with a proinflammatory and coagulopathic state leads to complex multisystemic complications.

Study on awareness and management based health action using video intervention (SAMBHAV) for postpartum depression among mothers attending immunisation clinic in a tertiary medical college hospital: Study protocol.

Introduction: Pregnancy exerts a detrimental effect on women's mental health. Maternal mental health is considered as one of the public health concerns as it impacts the health of both mother and the child. One in five people in developing countries experience serious mental health issues during pregnancy and after giving birth.

ACR Appropriateness Criteria® Congenital or Acquired Heart Disease.

Pediatric heart disease is a large and diverse field with an overall prevalence estimated at 6 to 13 per 1,000 live births. This document discusses appropriateness of advanced imaging for a broad range of variants. Diseases covered include tetralogy of Fallot, transposition of great arteries, congenital or acquired pediatric coronary artery abnormality, single ventricle, aortopathy, anomalous pulmonary venous return, aortopathy and aortic coarctation, with indications for advanced imaging spanning the entire natural history of the disease in children and adults, including initial diagnosis, treatment planning, treatment monitoring, and early detection of complications.

Ionizing Radiation Sensing with Functionalized and Copper-Coated SWCNT/PMMA Thin Film Nanocomposites.

This paper studies the ionizing radiation effects on functionalized single-walled carbon nanotube (SWCNT)/poly(methyl methacrylate) (PMMA) thin-film nanocomposites [SWNT/PMMA]. The functionalized thin-film devices are made of ferrocene-doped SWCNTs, SWCNTs functionalized with carboxylic acid (COOH), and SWCNTs coated/ modified with copper. The nanocomposite was synthesized by the solution blending method and the resulting nanocomposite was spin-cast on interdigitated electrodes (IDEs).

PET/MRI in colorectal and anal cancers: an update.

Positron emission tomography (PET) in the era of personalized medicine has a unique role in the management of oncological patients and offers several advantages over standard anatomical imaging. However, the role of molecular imaging in lower GI malignancies has historically been limited due to suboptimal anatomical evaluation on the accompanying CT, as well as significant physiological F-flurodeoxyglucose (FDG) uptake in the bowel. In the last decade, technological advancements have made whole-body FDG-PET/MRI a feasible alternative to PET/CT and MRI for lower GI malignancies.

Magnetic resonance radiomic feature performance in pulmonary nodule classification and impact of segmentation variability on radiomics.

Objective: Investigate the performance of multiparametric MRI radiomic features, alone or combined with current standard-of-care methods, for pulmonary nodule classification. Assess the impact of segmentation variability on feature reproducibility and reliability.

Methods: Radiomic features were extracted from 74 pulmonary nodules of 68 patients who underwent nodule resection or biopsy after MRI exam.

Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: a proof-of-concept study.

Purpose: To determine if pancreas radiomics-based AI model can detect the CT imaging signature of type 2 diabetes (T2D).

Methods: Total 107 radiomic features were extracted from volumetrically segmented normal pancreas in 422 T2D patients and 456 age-matched controls. Dataset was randomly split into training (300 T2D, 300 control CTs) and test subsets (122 T2D, 156 control CTs).

Volumetric Pancreas Segmentation on Computed Tomography: Accuracy and Efficiency of a Convolutional Neural Network Versus Manual Segmentation in 3D Slicer in the Context of Interreader Variability of Expert Radiologists.

Purpose: This study aimed to compare accuracy and efficiency of a convolutional neural network (CNN)-enhanced workflow for pancreas segmentation versus radiologists in the context of interreader reliability.

Methods: Volumetric pancreas segmentations on a data set of 294 portal venous computed tomographies were performed by 3 radiologists (R1, R2, and R3) and by a CNN. Convolutional neural network segmentations were reviewed and, if needed, corrected ("corrected CNN [c-CNN]" segmentations) by radiologists.

Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.

Background & Aims: Our purpose was to detect pancreatic ductal adenocarcinoma (PDAC) at the prediagnostic stage (3-36 months before clinical diagnosis) using radiomics-based machine-learning (ML) models, and to compare performance against radiologists in a case-control study.

Methods: Volumetric pancreas segmentation was performed on prediagnostic computed tomography scans (CTs) (median interval between CT and PDAC diagnosis: 398 days) of 155 patients and an age-matched cohort of 265 subjects with normal pancreas. A total of 88 first-order and gray-level radiomic features were extracted and 34 features were selected through the least absolute shrinkage and selection operator-based feature selection method.

PET/CT and PET/MRI in neuroendocrine neoplasms.

Advanced molecular imaging has come to play an integral role in the management of gastro-entero-pancreatic neuroendocrine neoplasms (GEP-NENs). Somatostatin receptor (SSTR) PET has now emerged as the reference standard for the evaluation of NENs and is particularly critical in the context of peptide receptor radionuclide therapy (PRRT) eligibility. SSTR PET/MRI with liver-specific contrast agent has a strong potential for one-stop-shop multiparametric evaluation of GEP-NENs.

Utilization of Lean & Six Sigma quality initiatives in Indian healthcare sector.

Purpose: The purpose of this paper is to investigate the utilization of Lean & Six Sigma quality initiatives in healthcare sector in India.

Methodology: The survey questionnaires were sent to 454 hospitals through registered postal in all the states of India. The survey questionnaire was designed to assess different quality initiatives; currently implemented in Indian hospitals, factors align with organization's objectives, reasons for not implementing Lean & Six Sigma and contribution of Lean & Six Sigma projects in healthcare improvement projects etc.

PSMA as a Theranostic Target in Hepatocellular Carcinoma: Immunohistochemistry and Ga-PSMA-11 PET Using Cyclotron-Produced Ga.

Prostate-specific membrane antigen (PSMA) is a validated target for molecular diagnostics and targeted radionuclide therapy. Our purpose was to evaluate PSMA expression in hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), and hepatic adenoma (HCA); investigate the genetic pathways in HCC associated with PSMA expression; and evaluate HCC detection rate with Ga-PSMA-11 positron emission tomography (PET). In phase 1, PSMA immunohistochemistry (IHC) on HCC (n = 148), CCA (n = 111), and HCA (n = 78) was scored.

Atypical Metastases in the Abdomen and Pelvis From Biochemically Recurrent Prostate Cancer: C-Choline PET/CT With Multimodality Correlation.

PET with targeted radiotracers has become integral to mapping the location and burden of recurrent disease in patients with biochemical recurrence (BCR) of prostate cancer (PCa). PET with C-choline is part of the National Comprehensive Cancer Network and European Association of Urology guidelines for evaluation of BCR. With advances in PET technology, increasing use of targeted radiotracers, and improved survival of patients with BCR because of novel therapeutics, atypical sites of metastases are being increasingly encountered, challenging the conventional view that prostate cancer rarely metastasizes beyond bones or lymph nodes.

Quality gaps in public pancreas imaging datasets: Implications & challenges for AI applications.

Objective: Quality gaps in medical imaging datasets lead to profound errors in experiments. Our objective was to characterize such quality gaps in public pancreas imaging datasets (PPIDs), to evaluate their impact on previously published studies, and to provide post-hoc labels and segmentations as a value-add for these PPIDs.

Methods: We scored the available PPIDs on the medical imaging data readiness (MIDaR) scale, and evaluated for associated metadata, image quality, acquisition phase, etiology of pancreas lesion, sources of confounders, and biases.

Radon and thoron levels in the dwellings of Buddonithanda: a village in the environs of proposed uranium mining site, Nalgonda district, Telangana state, India.

Elevated levels of radon and thoron in the indoor atmosphere may cause the deleterious effects on the mankind. Mining sites and their environs attract a special interest in radon studies as higher levels are frequently reported in the habitats. In the present study, radon and thoron levels were measured in the dwellings of Buddonithanda, a village in the environs of proposed uranium mining site, with pin-hole (SSNTDs) dosimeters for the period of a year.

Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset.

Purpose: To develop a two-stage three-dimensional (3D) convolutional neural networks (CNNs) for fully automated volumetric segmentation of pancreas on computed tomography (CT) and to further evaluate its performance in the context of intra-reader and inter-reader reliability at full dose and reduced radiation dose CTs on a public dataset.

Methods: A dataset of 1994 abdomen CT scans (portal venous phase, slice thickness ≤ 3.75-mm, multiple CT vendors) was curated by two radiologists (R1 and R2) to exclude cases with pancreatic pathology, suboptimal image quality, and image artifacts (n = 77).