Diagnostic performance of contrast-enhanced MR for acute appendicitis and alternative causes of abdominal pain in children

2014 ◽  
Vol 44 (8) ◽  
pp. 948-955 ◽  
Author(s):  
Jeffrey L. Koning ◽  
John H. Naheedy ◽  
Peter G. Kruk
Keyword(s):  
Abdominal Pain ◽  
Acute Appendicitis ◽  
Diagnostic Performance ◽  
Contrast Enhanced

scholarly journals Ectopic Perforated Appendicitis in Supraumbilical Region in a Case of Incomplete Intestinal Rotation

2021 ◽  
Vol 10 (31) ◽  
pp. 2521-2524
Author(s):  
Bhimarao Bhimarao ◽  
Rashmi Mysore Nagaraju ◽  
Lingaraj B. Patil
Keyword(s):  
Abdominal Pain ◽  
Acute Appendicitis ◽  
Perforated Appendicitis ◽  
Intestinal Malrotation ◽  
Emergency Laparotomy ◽  
Markers Of Inflammation ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Common Causes ◽  
Uncommon Condition

Acute appendicitis is one of the most common causes of acute abdominal pain and the most common condition requiring emergency surgery. Intestinal malrotation is a relatively uncommon condition. Depending upon the location of the cecum and appendix, patients with acute appendicitis in intestinal malrotation present atypically with abdominal pain localized on the site of appendicitis. Due to atypical presentation of central abdominal pain, other differentials presenting in this region should be excluded and accurate diagnosis should be made. We present a patient who came with central abdominal pain with elevated markers of inflammation. Contrast enhanced CT of abdomen in this patient revealed ectopic appendicitis located in supraumbilical region with signs of incomplete rotation of the bowel. CT played a pivotal role in identifying the underlying rotational abnormality of bowel, in localizing the inflamed appendix, identifying complications (perforation) and excluding other possible intra-abdominal pathologies. It was also helpful in surgical planning. Emergency laparotomy with appendectomy and lavage were performed on this patient who subsequently recovered.

Diagnostic performance of a biomarker panel as a negative predictor for acute appendicitis in adult ED patients with abdominal pain

2017 ◽  
Vol 35 (3) ◽  
pp. 418-424 ◽  
Author(s):  
David S. Huckins ◽  
Karen Copeland ◽  
Wesley Self ◽  
Cheryl Vance ◽  
Phyllis Hendry ◽  
...  
Keyword(s):  
Abdominal Pain ◽  
Acute Appendicitis ◽  
Diagnostic Performance ◽  
Biomarker Panel ◽  
Negative Predictor

Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis – a machine learning study

2021 ◽  
pp. 197140092199897
Author(s):  
Sarv Priya ◽  
Caitlin Ward ◽  
Thomas Locke ◽  
Neetu Soni ◽  
Ravishankar Pillenahalli Maheshwarappa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Central Nervous System ◽  
Nervous System ◽  
Diagnostic Performance ◽  
Model Performance ◽  
Area Under The Curve ◽  
Central Nervous System Lymphoma ◽  
First Order ◽  
Single Slice ◽  
Contrast Enhanced

Objectives To evaluate the diagnostic performance of multiple machine learning classifier models derived from first-order histogram texture parameters extracted from T1-weighted contrast-enhanced images in differentiating glioblastoma and primary central nervous system lymphoma. Methods Retrospective study with 97 glioblastoma and 46 primary central nervous system lymphoma patients. Thirty-six different combinations of classifier models and feature selection techniques were evaluated. Five-fold nested cross-validation was performed. Model performance was assessed for whole tumour and largest single slice using receiver operating characteristic curve. Results The cross-validated model performance was relatively similar for the top performing models for both whole tumour and largest single slice (area under the curve 0.909–0.924). However, there was a considerable difference between the worst performing model (logistic regression with full feature set, area under the curve 0.737) and the highest performing model for whole tumour (least absolute shrinkage and selection operator model with correlation filter, area under the curve 0.924). For single slice, the multilayer perceptron model with correlation filter had the highest performance (area under the curve 0.914). No significant difference was seen between the diagnostic performance of the top performing model for both whole tumour and largest single slice. Conclusions T1 contrast-enhanced derived first-order texture analysis can differentiate between glioblastoma and primary central nervous system lymphoma with good diagnostic performance. The machine learning performance can vary significantly depending on the model and feature selection methods. Largest single slice and whole tumour analysis show comparable diagnostic performance.

scholarly journals Acute appendicitis as an unusual cause of invasive ductal breast carcinoma metastasis

2020 ◽  
Vol 2020 (12) ◽  
Author(s):  
Vincent De Pauw ◽  
Julie Navez ◽  
Stephane Holbrechts ◽  
Jean Lemaitre
Keyword(s):  
Abdominal Pain ◽  
Breast Carcinoma ◽  
Acute Appendicitis ◽  
Ductal Carcinoma ◽  
Hormonal Treatment ◽  
Locoregional Therapy ◽  
Invasive Ductal Breast Carcinoma ◽  
Ductal Breast Carcinoma ◽  
History Of ◽  
Anticancer Treatment

Abstract Acute appendicitis is one of the most common causes of abdominal pain at the emergency room. In rare cases, it can be caused by malignancy, even metastatic lesions from extra-abdominal neoplasia. Herein, we report a case of a 64-year-old female with a history of invasive ductal carcinoma of the breast treated by chemotherapy, surgery, radiotherapy and hormonotherapy, relapsing several years later as a bone and a pleura metastasis successfully cured by locoregional therapy and hormonal treatment. She presented with acute abdominal pain without signs of peritonitis. Abdominal computed tomodensitometry showed sign of appendicitis. Therefore, laparoscopic exploration and appendicectomy was performed. During surgery, multiple peritoneal nodules were found and harvested. Pathology showed metastatic nodules of invasive ductal breast carcinoma, including in the appendicular wall, concluding to peritoneal carcinomatosis. The postoperative course was uneventful, but the patient died 1 year later after refusing anticancer treatment.

scholarly journals Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 330
Author(s):  
Mio Adachi ◽  
Tomoyuki Fujioka ◽  
Mio Mori ◽  
Kazunori Kubota ◽  
Yuka Kikuchi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Diagnostic Performance ◽  
Maximum Intensity Projection ◽  
Breast Mri ◽  
Magnetic Resonance Images ◽  
Maximum Intensity ◽  
Validation Data ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

Changing Incidence of Acute Appendicitis and Nonspecific Abdominal Pain Between 1987 and 2007 in Finland

2011 ◽  
Vol 35 (4) ◽  
pp. 731-738 ◽  
Author(s):  
Imre Ilves ◽  
Hannu E. K. Paajanen ◽  
Karl-Heinz Herzig ◽  
Anne Fagerström ◽  
Pekka J. Miettinen
Keyword(s):  
Abdominal Pain ◽  
Acute Appendicitis

scholarly journals Improvement of the Diagnostic Performance of Facial Neuritis Using Contrast-Enhanced 3D T1 Black-Blood Imaging: Comparison with Contrast-Enhanced 3D T1-Spoiled Gradient-Echo Imaging

2021 ◽  
Vol 10 (9) ◽  
pp. 1850
Author(s):  
Seun-Ah Lee ◽  
Sang-Won Jo ◽  
Suk-Ki Chang ◽  
Ki-Han Kwon
Keyword(s):  
Quantitative Analysis ◽  
Diagnostic Performance ◽  
Spin Echo ◽  
Fast Spin Echo ◽  
Black Blood ◽  
Gradient Echo ◽  
Diagnostic Confidence ◽  
Contrast Enhanced ◽  
Black Blood Imaging ◽  
Spoiled Gradient Echo

This study aims to investigate the diagnostic ability of the contrast-enhanced 3D T1 black-blood fast spin-echo (T1 BB-FSE) sequence compared with the contrast-enhanced 3D T1-spoiled gradient-echo (CE-GRE) sequence in patients with facial neuritis. Forty-five patients with facial neuritis who underwent temporal bone MR imaging, including T1 BB-FSE and CE-GRE imaging, were examined. Two reviewers independently assessed the T1 BB-FSE and CE-GRE images in terms of diagnostic performance, and qualitative (diagnostic confidence and visual asymmetric enhancement) and quantitative analysis (contrast-enhancing lesion extent of the canalicular segment of the affected facial nerve (LEC) and the affected side-to-normal signal intensity ratio (rSI)). The AUCs of each reviewer, and the sensitivity and accuracy of T1 BB-FSE were significantly superior to those of CE-GRE (p < 0.05). Regarding diagnostic confidence and visual asymmetric enhancement, T1 BB-FSE tended to be rated greater than CE-GRE (p < 0.05). Additionally, in quantitative analysis, LEC and rSI of the canalicular segment on T1 BB-FSE were larger than those on CE-GRE (p < 0.05). The T1 BB-FSE sequence was significantly superior to the CE-GRE sequence, with more conspicuous lesion visualization in terms of both qualitative and quantitative aspects in patients with facial neuritis.

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