16 Architectural Distortion

2017 ◽  
Keyword(s):  
Architectural Distortion

A textural approach for recognizing architectural distortion in mammograms

2013 ◽  
Author(s):  
Elham Mohammadi ◽  
Emad Fatemizadeh ◽  
Hamid Sheikhzadeh ◽  
Sahar Khoubani
Keyword(s):  
Architectural Distortion

Detection of architectural distortion in mammograms acquired prior to the detection of breast cancer using texture and fractal analysis

2008 ◽  
Author(s):  
Shormistha Prajna ◽  
Rangaraj M. Rangayyan ◽  
Fábio J. Ayres ◽  
J. E. Leo Desautels
Keyword(s):  
Breast Cancer ◽  
Fractal Analysis ◽  
Architectural Distortion

A new feature selection method for the detection of architectural distortion in mammographic images

2016 ◽  
Author(s):  
Xiaoming Liu ◽  
Leilei Zhai ◽  
Ting Zhu ◽  
Kai Zhang
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Architectural Distortion ◽  
Mammographic Images ◽  
New Feature

Detection of architectural distortion from the ridges in a digitized mammogram

2018 ◽  
Vol 12 (7) ◽  
pp. 1285-1292 ◽  
Author(s):  
Yusuf Akhtar ◽  
Dipti Prasad Mukherjee
Keyword(s):  
Architectural Distortion

Gastrointestinal Adverse Events Induced by Immune-Checkpoint Inhibitors

Digestion ◽  
2021 ◽  
pp. 1-9
Author(s):  
Shunichi Yanai ◽  
Yosuke Toya ◽  
Tamotsu Sugai ◽  
Takayuki Matsumoto
Keyword(s):  
Adverse Events ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Malignant Tumors ◽  
Checkpoint Inhibitors ◽  
Architectural Distortion ◽  
Management Guidelines ◽  
Crypt Abscess ◽  
Gastrointestinal Adverse Events ◽  
Histopathologic Findings

<b><i>Background:</i></b> As immune-checkpoint inhibitors (ICI) are becoming standard therapies for malignant tumors, increasing attention has been paid to their associated immune-related adverse events (irAEs). The gastrointestinal tract is the major site of irAEs, and it has recently become evident that the large bowel is the most frequently affected region. The aim of this narrative review was to clarify the endoscopic and histopathologic findings of and treatments for ICI-induced colitis. <b><i>Summary:</i></b> Endoscopic findings of ICI-induced colitis include a reddish, edematous mucosa with increased mucous exudate, loss of normal vascularity, and a granular mucosa with or without mucosal breaks. Histopathologic findings of ICI-induced colitis are expansion of the lamina propria, intraepithelial infiltration of neutrophils, crypt architectural distortion, neutrophilic crypt abscess, and prominent apoptosis. The clinical, endoscopic, and histopathologic severity of ICI-induced colitis is diverse, but colonoscopy together with biopsy is necessary for diagnosis. While a certain proportion of patients with ICI-induced colitis have an intractable clinical course, management guidelines are based on retrospective analyses. Prospective studies are needed to assess the efficacy of various medications, including immunosuppressive regimens. <b><i>Key Messages:</i></b> Colonoscopy with biopsy is the gold standard for the diagnosis of ICI-induced colitis. Endoscopists should be aware of the clinical features and pathophysiology of ICI-induced colitis for prompt diagnosis and treatment planning.

Automatic detection of various patterns of calcification distribution on mammograms: a graph convolution network approach (Preprint)

2021 ◽  
Author(s):  
Melissa Min-Szu Yao ◽  
Hao Du ◽  
Mikael Hartman ◽  
Wing P. Chan ◽  
Mengling Feng
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Automatic Detection ◽  
Ground Truth ◽  
Architectural Distortion ◽  
Breast Cancers ◽  
Convolutional Network ◽  
Mammographic Images

UNSTRUCTURED Purpose: To develop a novel artificial intelligence (AI) model algorithm focusing on automatic detection and classification of various patterns of calcification distribution in mammographic images using a unique graph convolution approach. Materials and methods: Images from 200 patients classified as Category 4 or 5 according to the American College of Radiology Breast Imaging Reporting and Database System, which showed calcifications according to the mammographic reports and diagnosed breast cancers. The calcification distributions were classified as either diffuse, segmental, regional, grouped, or linear. Excluded were mammograms with (1) breast cancer as a single or combined characterization such as a mass, asymmetry, or architectural distortion with or without calcifications; (2) hidden calcifications that were difficult to mark; or (3) incomplete medical records. Results: A graph convolutional network-based model was developed. 401 mammographic images from 200 cases of breast cancer were divided based on calcification distribution pattern: diffuse (n = 24), regional (n = 111), group (n = 201), linear (n = 8) or segmental (n = 57). The classification performances were measured using metrics including precision, recall, F1 score, accuracy and multi-class area under receiver operating characteristic curve. The proposed achieved precision of 0.483 ± 0.015, sensitivity of 0.606 (0.030), specificity of 0.862 ± 0.018, F1 score of 0.527 ± 0.035, accuracy of 60.642% ± 3.040% and area under the curve of 0.754 ± 0.019, finding method to be superior compared to all baseline models. The predicted linear and diffuse classifications were highly similar to the ground truth, and the predicted grouped and regional classifications were also superior compared to baseline models. Conclusion: The proposed deep neural network framework is an AI solution to automatically detect and classify calcification distribution patterns on mammographic images highly suspected of showing breast cancers. Further study of the AI model in an actual clinical setting and additional data collection will improve its performance.

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