scholarly journals Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1224
Author(s):  
Francesco Bianconi ◽  
Mario Luca Fravolini ◽  
Isabella Palumbo ◽  
Giulia Pascoletti ◽  
Susanna Nuvoli ◽  
...  
Keyword(s):  
Predictive Models ◽  
Clinical Decision Making ◽  
Texture Features ◽  
Lung Nodules ◽  
Computer Assisted ◽  
Imaging Features ◽  
Grey Level ◽  
Difference Matrix ◽  
Research Attention ◽  
The Stability

Computer-assisted analysis of three-dimensional imaging data (radiomics) has received a lot of research attention as a possible means to improve the management of patients with lung cancer. Building robust predictive models for clinical decision making requires the imaging features to be stable enough to changes in the acquisition and extraction settings. Experimenting on 517 lung lesions from a cohort of 207 patients, we assessed the stability of 88 texture features from the following classes: first-order (13 features), Grey-level Co-Occurrence Matrix (24), Grey-level Difference Matrix (14), Grey-level Run-length Matrix (16), Grey-level Size Zone Matrix (16) and Neighbouring Grey-tone Difference Matrix (five). The analysis was based on a public dataset of lung nodules and open-access routines for feature extraction, which makes the study fully reproducible. Our results identified 30 features that had good or excellent stability relative to lesion delineation, 28 to intensity quantisation and 18 to both. We conclude that selecting the right set of imaging features is critical for building clinical predictive models, particularly when changes in lesion delineation and/or intensity quantisation are involved.

Primary clinical study of radiomics for diagnosing simple bone cyst of the jaw

2021 ◽  
pp. 20200384
Author(s):  
Zhe-Yi Jiang ◽  
Tian-Jun Lan ◽  
Wei-Xin Cai ◽  
Qian Tao
Keyword(s):  
Bone Cyst ◽  
Maxillofacial Surgery ◽  
Correlation Coefficients ◽  
Texture Features ◽  
Image Features ◽  
Control Group ◽  
Imaging Features ◽  
Bone Cysts ◽  
Difference Matrix ◽  
Occurrence Matrix

Objective: To screen the radiomic features of simple bone cysts of the jaws and explore the potential application of radiomics in pre-operative diagnosis of jaw simple bone cysts. Methods: The investigators designed and implemented a case–control study. 19 patients with simple bone cysts who were admitted to the Department of Maxillofacial Surgery, Sun Yat-sen University Affiliated Stomatology Hospital from 2013 to 2019 were included in this study. Their clinical data and cone-beam computed tomography (CBCT) images were examined. The control group consisted of patients with odontogenic keratocyst. CBCT imaging features were analyzed and compared between the patient and control groups. Results: Overall, 10,323 image features were extracted through feature analysis. A subset of 25 radiomic features obtained after feature selection were analyzed further. These 25 features were significantly different between the 2 groups (p < 0.05). The absolute value of correlation coefficient was 0.487–0.775. Gray-level co-occurrence matrix (GLCM) contrast, neighborhood gray tone difference matrix (NGTDM) contrast, and GLCM variance were the features with the highest correlation coefficients. Conclusions: Pre-operative radiomics analysis showed the differences between simple bone cysts and odontogenic keratocysts, can help to diagnose simple bone cysts. Three specific texture features—GLCM contrast, NGTDM contrast, and GLCM variance—may be the characteristic imaging features of simple bone cysts of the jaw.

A COMPUTER-AIDED SYSTEM FOR MASS DETECTION AND CLASSIFICATION IN DIGITIZED MAMMOGRAMS

2005 ◽  
Vol 17 (05) ◽  
pp. 215-228 ◽  
Author(s):  
SHENG-CHIH YANG ◽  
CHUIN-MU WANG ◽  
YI-NUNG CHUNG ◽  
GIU-CHENG HSU ◽  
SAN-KAN LEE ◽  
...  
Keyword(s):  
Probabilistic Neural Network ◽  
Diagnostic System ◽  
Region Of Interest ◽  
Texture Features ◽  
Computer Assisted ◽  
Mass Detection ◽  
Difference Matrix ◽  
Data Set ◽  
System A ◽  
Mass Classification

This paper presents a computer-assisted diagnostic system for mass detection and classification, which performs mass detection on regions of interest followed by the benign-malignant classification on detected masses. In order for mass detection to be effective, a sequence of preprocessing steps are designed to enhance the intensity of a region of interest, remove the noise effects and locate suspicious masses using five texture features generated from the spatial gray level difference matrix (SGLDM) and fractal dimension. Finally, a probabilistic neural network (PNN) coupled with entropic thresholding techniques is developed for mass extraction. Since the shapes of masses are crucial in classification between benignancy and malignancy, four shape features are further generated and joined with the five features previously used in mass detection to be implemented in another PNN for mass classification. To evaluate our designed system a data set collected in the Taichung Veteran General Hospital, Taiwan, R.O.C. was used for performance evaluation. The results are encouraging and have shown promise of our system.

scholarly journals PET/CT Radiomics in Lung Cancer: An Overview

2020 ◽  
Vol 10 (5) ◽  
pp. 1718 ◽  
Author(s):  
Francesco Bianconi ◽  
Isabella Palumbo ◽  
Angela Spanu ◽  
Susanna Nuvoli ◽  
Mario Luca Fravolini ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Large Scale ◽  
Field Analysis ◽  
Response To Treatment ◽  
Computer Assisted ◽  
Imaging Features ◽  
Research Attention ◽  
Full Field ◽  
Pet Ct ◽  
Potential Applications

Quantitative extraction of imaging features from medical scans (‘radiomics’) has attracted a lot of research attention in the last few years. The literature has consistently emphasized the potential use of radiomics for computer-assisted diagnosis, as well as for predicting survival and response to treatment. Radiomics is appealing in that it enables full-field analysis of the lesion, provides nearly real-time results, and is non-invasive. Still, a lot of studies suffer from a series of drawbacks such as lack of standardization and repeatability. Such limitations, along with the unmet demand for large enough image datasets for training the algorithms, are major hurdles that still limit the application of radiomics on a large scale. In this paper, we review the current developments, potential applications, limitations, and perspectives of PET/CT radiomics with specific focus on the management of patients with lung cancer.

scholarly journals Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab

2016 ◽  
Vol 18 (12) ◽  
pp. 1680-1687 ◽  
Author(s):  
Ken Chang ◽  
Biqi Zhang ◽  
Xiaotao Guo ◽  
Min Zong ◽  
Rifaquat Rahman ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Progression Free Survival ◽  
Clinical Decision ◽  
Recurrent Glioblastoma ◽  
Machine Learning Techniques ◽  
Imaging Biomarkers ◽  
Imaging Features ◽  
Mri Features ◽  
Fluid Attenuated Inversion Recovery ◽  
Multimodal Mri

Abstract Background Bevacizumab is a humanized antibody against vascular endothelial growth factor approved for treatment of recurrent glioblastoma. There is a need to discover imaging biomarkers that can aid in the selection of patients who will likely derive the most survival benefit from bevacizumab. Methods The aim of the study was to examine if pre- and posttherapy multimodal MRI features could predict progression-free survival and overall survival (OS) for patients with recurrent glioblastoma treated with bevacizumab. The patient population included 84 patients in a training cohort and 42 patients in a testing cohort, separated based on pretherapy imaging date. Tumor volumes of interest were segmented from contrast-enhanced T1-weighted and fluid attenuated inversion recovery images and were used to derive volumetric, shape, texture, parametric, and histogram features. A total of 2293 pretherapy and 9811 posttherapy features were used to generate the model. Results Using standard radiographic assessment criteria, the hazard ratio for predicting OS was 3.38 (P < .001). The hazard ratios for pre- and posttherapy features predicting OS were 5.10 (P < .001) and 3.64 (P < .005) for the training and testing cohorts, respectively. Conclusion With the use of machine learning techniques to analyze imaging features derived from pre- and posttherapy multimodal MRI, we were able to develop a predictive model for patient OS that could potentially assist clinical decision making.

scholarly journals Bayesian Network Models with Decision Tree Analysis for Management of Childhood Malaria in Malawi

2020 ◽  
Author(s):  
Sanya B. Taneja ◽  
Gerald P. Douglas ◽  
Gregory F. Cooper ◽  
Marian G. Michaels ◽  
Marek J. Druzdzel ◽  
...  
Keyword(s):  
Decision Tree ◽  
Bayesian Network ◽  
Clinical Features ◽  
Predictive Models ◽  
Clinical Decision Making ◽  
Performance Metrics ◽  
Expert Knowledge ◽  
Clinical Decision ◽  
Automated Method ◽  
Childhood Malaria

Abstract Background: Malaria is a major cause of death in children under five years old in low- and middle-income countries such as Malawi. Accurate diagnosis and management of malaria can help reduce the global burden of childhood morbidity and mortality. Trained healthcare workers in rural health centers manage malaria with limited supplies of malarial diagnostic tests and drugs for treatment. A clinical decision support system that integrates predictive models to provide an accurate prediction of malaria based on clinical features could aid healthcare worker in judicious use of testing and treatment. We developed Bayesian network (BN) models to predict the probability of malaria from clinical features and an illustrative decision tree to model the decision to use or not use a malaria rapid diagnostic test (mRDT).Methods: We developed two BN models from data that were collected in a national survey of outpatient encounters of children in Malawi. The target diagnosis is taken as the result of mRDT. The first BN model was created manually with expert knowledge, and the second model was derived using an automated method followed by modifications guided by expert knowledge. The performance of the BN models was compared to other statistical models on a range of performance metrics. We developed a decision tree that integrates predictions from these predictive models with the costs of mRDT and a course of recommended treatment. Results: Compared to the logistic regression and random forest models, the BN models had similar accuracy of 64% but had higher sensitivity at the cost of lower specificity at the default threshold. Sensitivity analysis of the decision tree showed that at low (below 0.04) and high (above 0.4) probabilities of malaria in a child, the preferred decision that minimizes expected costs is not to perform mRDT.Conclusion: In resource-constrained settings, judicious use of mRDT is important. Predictive models in combination with decision analysis can provide personalized guidance on when to use mRDT in the management of childhood malaria. BN models can be efficiently derived from data to support such clinical decision making.

scholarly journals Analysis of texture features for wood defect classification

2020 ◽  
Vol 9 (1) ◽  
pp. 121-128
Author(s):  
Nur Dalila Abdullah ◽  
Ummi Raba'ah Hashim ◽  
Sabrina Ahmad ◽  
Lizawati Salahuddin
Keyword(s):  
Classification Accuracy ◽  
Visual Inspection ◽  
Visual Analysis ◽  
Computational Cost ◽  
Texture Features ◽  
Feature Analysis ◽  
Defect Classification ◽  
Grey Level ◽  
Automated Visual Inspection ◽  
Accuracy Measures

Selecting important features in classifying wood defects remains a challenging issue to the automated visual inspection domain. This study aims to address the extraction and analysis of features based on statistical texture on images of wood defects. A series of procedures including feature extraction using the Grey Level Dependence Matrix (GLDM) and feature analysis were executed in order to investigate the appropriate displacement and quantisation parameters that could significantly classify wood defects. Samples were taken from the KembangSemangkuk (KSK), Meranti and Merbau wood species. Findings from visual analysis and classification accuracy measures suggest that the feature set with the displacement parameter, d=2, and quantisation level, q=128, shows the highest classification accuracy. However, to achieve less computational cost, the feature set with quantisation level, q=32, shows acceptable performance in terms of classification accuracy.

Psychopharmacological treatments strategies in consultation-liaison psychiatry: Clinical vignette and pros and cons

2016 ◽  
Vol 33 (S1) ◽  
pp. S61-S61
Author(s):  
G. Mattei ◽  
N. Colombini ◽  
S. Ferrari ◽  
G.M. Galeazzi
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Vignette ◽  
Pharmacological Intervention ◽  
Computer Assisted ◽  
Liaison Psychiatry ◽  
Pros And Cons ◽  
Consultation Liaison Psychiatry ◽  
Ward Personnel ◽  
Computer Assisted Decision Making

IntroductionMultimorbidity and polipharmacotherapy are crucial features influencing the psychiatrist's prescription in the consultation-liaison psychiatry (CLP) setting.Aimsto provide an example of computer-assisted decision-making in psychotropic prescriptions and to provide hints for developing pharmacological treatment strategies in the CLP setting.MethodsCase report. A clinical vignette is presented, followed by a review of available online computer-assisted prescription software.ResultsA woman in her seventies was repeatedly referred for psychiatric consultation. Eleven different medications were administered daily, because of multimorbidity. A diagnosis of distymia was established, with comorbid mixed pain (partly fulfilling the criteria of somatic symptom disorder) and substance use disorder (opioids). After the first assessment, six follow-up visits were needed during hospitalization. Mirtazapine and benzodiazepines were introduced. Beside the pharmacological intervention, conflict mediation was performed in the relationship with the patient, her relatives, the ward personnel and the GP, to develop a long-term rehabilitation project. Pros and cons of online computer-assisted prescription software were discussed together with the ward personnel, as well.ConclusionsComputer-assisted decision-making in psychotropic prescription is becoming more common and feasible. The use of available software may contribute to safety, effectiveness and cost-effectiveness of clinical decision-making. Risks are also possible: depending for example from regional differences in prescription indications, different guidelines, pharmacogenomics, frequency with which databases are updated, sponsorships, possible conflicts of interest, and real clinical significance of highlighted interactions – all issues the clinician willing to benefit from this modern tools should pay attention to.Disclosure of interestThe authors have not supplied their declaration of competing interest.

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