1
|
Varghese BA, Lee S, Cen S, Talebi A, Mohd P, Stahl D, Perkins M, Desai B, Duddalwar VA, Larsen LH. Characterizing breast masses using an integrative framework of machine learning and CEUS-based radiomics. J Ultrasound 2022; 25:699-708. [PMID: 35040103 PMCID: PMC9402818 DOI: 10.1007/s40477-021-00651-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 12/14/2021] [Indexed: 12/28/2022] Open
Abstract
AIMS We evaluated the performance of contrast-enhanced ultrasound (CEUS) based on radiomics analysis to distinguish benign from malignant breast masses. METHODS 131 women with suspicious breast masses (BI-RADS 4a, 4b, or 4c) who underwent CEUS examinations (using intravenous injection of perflutren lipid microsphere or sulfur hexafluoride lipid-type A microspheres) prior to ultrasound-guided biopsies were retrospectively identified. Post biopsy pathology showed 115 benign and 16 malignant masses. From the cine clip of the CEUS exams obtained using the built-in GE scanner software, breast masses and adjacent normal tissue were then manually segmented using the ImageJ software. One frame representing each of the four phases: precontrast, early, peak, and delay enhancement were selected post segmentation from each CEUS clip. 112 radiomic metrics were extracted from each segmented tissue normalized breast mass using custom Matlab® code. Linear and nonlinear machine learning (ML) methods were used to build the prediction model to distinguish benign from malignant masses. tenfold cross-validation evaluated model performance. Area under the curve (AUC) was used to quantify prediction accuracy. RESULTS Univariate analysis found 35 (38.5%) radiomic variables with p < 0.05 in differentiating between benign from malignant masses. No feature selection was performed. Predictive models based on AdaBoost reported an AUC = 0.72 95% CI (0.56, 0.89), followed by Random Forest with an AUC = 0.71 95% CI (0.56, 0.87). CONCLUSIONS CEUS based texture metrics can distinguish between benign and malignant breast masses, which can, in turn, lead to reduced unnecessary breast biopsies.
Collapse
Affiliation(s)
- Bino A Varghese
- Keck School of Medicine, University of Southern California, 1441 Eastlake Avenue, Ground Floor, G360, Los Angeles, CA, 90033, USA.
| | - Sandy Lee
- Keck School of Medicine, University of Southern California, 1441 Eastlake Avenue, Ground Floor, G360, Los Angeles, CA, 90033, USA
| | - Steven Cen
- Keck School of Medicine, University of Southern California, 1441 Eastlake Avenue, Ground Floor, G360, Los Angeles, CA, 90033, USA
| | - Amir Talebi
- Keck School of Medicine, University of Southern California, 1441 Eastlake Avenue, Ground Floor, G360, Los Angeles, CA, 90033, USA
| | - Passant Mohd
- Keck School of Medicine, University of Southern California, 1441 Eastlake Avenue, Ground Floor, G360, Los Angeles, CA, 90033, USA
| | - Daniel Stahl
- Keck School of Medicine, University of Southern California, 1441 Eastlake Avenue, Ground Floor, G360, Los Angeles, CA, 90033, USA
| | - Melissa Perkins
- Keck School of Medicine, University of Southern California, 1441 Eastlake Avenue, Ground Floor, G360, Los Angeles, CA, 90033, USA
| | - Bhushan Desai
- Keck School of Medicine, University of Southern California, 1441 Eastlake Avenue, Ground Floor, G360, Los Angeles, CA, 90033, USA
| | - Vinay A Duddalwar
- Keck School of Medicine, University of Southern California, 1441 Eastlake Avenue, Ground Floor, G360, Los Angeles, CA, 90033, USA
| | - Linda H Larsen
- Keck School of Medicine, University of Southern California, 1441 Eastlake Avenue, Ground Floor, G360, Los Angeles, CA, 90033, USA
| |
Collapse
|
2
|
Schäfer-Somi S. Diseases of the Canine Prostate Gland. Vet Med Sci 2022. [DOI: 10.5772/intechopen.105835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
In dogs, the most frequent diseases of the prostate gland are benign prostate gland hyperplasia (BPH), acute and chronic prostatitis, squamous metaplasia, and prostate tumors. New diagnostic tools comprise diagnostic markers in the blood and urine, as well as advanced imaging methods. The therapy can be initialized with the 5α-reductase-inhibitor finasteride or an anti-androgenic compound, and prolonged with a long-acting gonadotropin-releasing-hormone (GnRH)-agonist such as deslorelin. In case of prostatitis, effective antibiotics must be applied for weeks. Antibiotics must be able to penetrate into the prostate tissue; fluoroquinolones, clindamycin, and erythromycin are good choices and are in addition effective against mycoplasms. The chronical prostatitis cannot be differentiated from a neoplasia by sonography; a biopsy, histological, and bacteriological examination are required. Tumors of the prostate gland are seldom and mostly occur in castrated but in intact dogs. For the final diagnosis, a biopsy must be taken. Partial and total resection of the prostate gland by use of laser technique is possible but coincedes with many side effects and the prognosis is still futile. Immunotherapy combined with NSAIDs, targeted noninvasive thermotherapy, BRAF gene inhibitors, or prostate artery chemoembolization are promising methods.
Collapse
|
3
|
Breast Tumor Classification Using Intratumoral Quantitative Ultrasound Descriptors. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1633858. [PMID: 35295204 PMCID: PMC8920646 DOI: 10.1155/2022/1633858] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 02/15/2022] [Accepted: 02/23/2022] [Indexed: 12/11/2022]
Abstract
Breast cancer is a global epidemic, responsible for one of the highest mortality rates among women. Ultrasound imaging is becoming a popular tool for breast cancer screening, and quantitative ultrasound (QUS) techniques are being increasingly applied by researchers in an attempt to characterize breast tissue. Several different quantitative descriptors for breast cancer have been explored by researchers. This study proposes a breast tumor classification system using the three major types of intratumoral QUS descriptors which can be extracted from ultrasound radiofrequency (RF) data: spectral features, envelope statistics features, and texture features. A total of 16 features were extracted from ultrasound RF data across two different datasets, of which one is balanced and the other is severely imbalanced. The balanced dataset contains RF data of 100 patients with breast tumors, of which 48 are benign and 52 are malignant. The imbalanced dataset contains RF data of 130 patients with breast tumors, of which 104 are benign and 26 are malignant. Holdout validation was used to split the balanced dataset into 60% training and 40% testing sets. Feature selection was applied on the training set to identify the most relevant subset for the classification of benign and malignant breast tumors, and the performance of the features was evaluated on the test set. A maximum classification accuracy of 95% and an area under the receiver operating characteristic curve (AUC) of 0.968 was obtained on the test set. The performance of the identified relevant features was further validated on the imbalanced dataset, where a hybrid resampling strategy was firstly utilized to create an optimal balance between benign and malignant samples. A maximum classification accuracy of 93.01%, sensitivity of 94.62%, specificity of 91.4%, and AUC of 0.966 were obtained. The results indicate that the identified features are able to distinguish between benign and malignant breast lesions very effectively, and the combination of the features identified in this research has the potential to be a significant tool in the noninvasive rapid and accurate diagnosis of breast cancer.
Collapse
|
4
|
Martorell A, Martin-Gorgojo A, Ríos-Viñuela E, Rueda-Carnero J, Alfageme F, Taberner R. [Translated article] Artificial intelligence in dermatology: A threat or an opportunity? ACTAS DERMO-SIFILIOGRAFICAS 2022. [DOI: 10.1016/j.ad.2021.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022] Open
|
5
|
Martorell A, Martin-Gorgojo A, Ríos-Viñuela E, Rueda-Carnero J, Alfageme F, Taberner R. Inteligencia artificial en dermatología: ¿amenaza u oportunidad? ACTAS DERMO-SIFILIOGRAFICAS 2022; 113:30-46. [DOI: 10.1016/j.ad.2021.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 07/18/2021] [Indexed: 11/25/2022] Open
|
6
|
Kolchev AA, Pasynkov DV, Egoshin IA, Kliouchkin IV, Pasynkova OO. Cystic (including atypical) and solid breast lesion classification using the different features of quantitative ultrasound parametric images. Int J Comput Assist Radiol Surg 2021; 17:219-228. [PMID: 34727337 DOI: 10.1007/s11548-021-02522-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 10/13/2021] [Indexed: 12/09/2022]
Abstract
PURPOSE The amount of ultrasound (US) breast examinations continues to grow rapidly because of the wider endorsement of breast cancer screening programs. Cysts are the most commonly diagnosed breast lesions. Atypical breast cysts can be a serious differentiation problem in the US. Our goal was to develop noninvasive automated US grayscale image analysis for the cystic and solid breast lesion differentiation based on mathematical image post-processing. MATERIALS AND METHODS We used a set of 217 ultrasound images of proven 107 cystic (including 53 atypical) and 110 solid lesions. Empirical statistical and morphological models of the lesions were used to obtain features. The AUC indicator and Student's t test were used to assess the quality of the individual features. The Pearson correlation matrix was used to calculate the correlation between various features. The LASSO and stepwise regression methods were used to determine the most significant features. Finally, the lesion classification was carried out by the various methods. RESULTS The use of LASSO regression for the feature selection made it possible to select the most significant features for classification. The sensitivity increased from 87.1% to 89.2% and the specificity-from 92.2 to 94.8%. After the correlation matrix construction, it was found that features with a high value of the correlation coefficient (0.72; 0.75) can also be used to improve the quality of the classification. CONCLUSION The construction of the empirical model of the lesion pixels brightness behavior can provide parameters that are important for the correct classification of ultrasound images. The optimal set of features with the maximum discriminant characteristics may not be consistent with the correlation of features and the value of the AUC index. Features with a low AUC index (in our case 0.72) can also be important for improving the quality of the classification.
Collapse
Affiliation(s)
- A A Kolchev
- Mari State University, Ministry of Education and Science of Russian Federation, 1 Lenin Square, Yoshkar-Ola, 424000, Russia
- Kazan (Volga region) Federal University, Ministry of Education and Science of Russian Federation, 18 Kremlevskaya St., Kazan, 420008, Russia
| | - D V Pasynkov
- Oncology Dispenser of Mari El Republic, Ministry of Health of Mari El Republic, 22 Osipenko St., Yoshkar-Ola, 424037, Russia
| | - I A Egoshin
- Mari State University, Ministry of Education and Science of Russian Federation, 1 Lenin Square, Yoshkar-Ola, 424000, Russia.
| | - I V Kliouchkin
- Kazan Medical University, Ministry of Health of Russian Federation, 49 Butlerova St., Kazan, 420012, Russia
| | - O O Pasynkova
- Mari State University, Ministry of Education and Science of Russian Federation, 1 Lenin Square, Yoshkar-Ola, 424000, Russia
| |
Collapse
|
7
|
Ryu H, Shin SY, Lee JY, Lee KM, Kang HJ, Yi J. Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning. Eur Radiol 2021; 31:8733-8742. [PMID: 33881566 PMCID: PMC8523410 DOI: 10.1007/s00330-021-07850-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/15/2021] [Accepted: 03/02/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES To develop a convolutional neural network system to jointly segment and classify a hepatic lesion selected by user clicks in ultrasound images. METHODS In total, 4309 anonymized ultrasound images of 3873 patients with hepatic cyst (n = 1214), hemangioma (n = 1220), metastasis (n = 1001), or hepatocellular carcinoma (HCC) (n = 874) were collected and annotated. The images were divided into 3909 training and 400 test images. Our network is composed of one shared encoder and two inference branches used for segmentation and classification and takes the concatenation of an input image and two Euclidean distance maps of foreground and background clicks provided by a user as input. The performance of hepatic lesion segmentation was evaluated based on the Jaccard index (JI), and the performance of classification was based on accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC). RESULTS We achieved performance improvements by jointly conducting segmentation and classification. In the segmentation only system, the mean JI was 68.5%. In the classification only system, the accuracy of classifying four types of hepatic lesions was 79.8%. The mean JI and classification accuracy were 68.5% and 82.2%, respectively, for the proposed joint system. The optimal sensitivity and specificity and the AUROC of classifying benign and malignant hepatic lesions of the joint system were 95.0%, 86.0%, and 0.970, respectively. The respective sensitivity, specificity, and the AUROC for classifying four hepatic lesions of the joint system were 86.7%, 89.7%, and 0.947. CONCLUSIONS The proposed joint system exhibited fair performance compared to segmentation only and classification only systems. KEY POINTS • The joint segmentation and classification system using deep learning accurately segmented and classified hepatic lesions selected by user clicks in US examination. • The joint segmentation and classification system for hepatic lesions in US images exhibited higher performance than segmentation only and classification only systems. • The joint segmentation and classification system could assist radiologists with minimal experience in US imaging by characterizing hepatic lesions.
Collapse
Affiliation(s)
- Hwaseong Ryu
- Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea
| | | | - Jae Young Lee
- Department of Radiology and the Institute of Radiation Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
| | - Kyoung Mu Lee
- Department of Electrical and Computer Engineering, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea.
| | - Hyo-Jin Kang
- Department of Radiology and the Institute of Radiation Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jonghyon Yi
- Medical Imaging R&D Group, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seoul, Republic of Korea
| |
Collapse
|
8
|
Martorell A, Martin-Gorgojo A, Ríos-Viñuela E, Rueda-Carnero J, Alfageme F, Taberner R. Artificial intelligence in dermatology: A threat or an opportunity? ACTAS DERMO-SIFILIOGRAFICAS 2021. [DOI: 10.1016/j.adengl.2021.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
|
9
|
Esmaeili N, Boese A, Davaris N, Arens C, Navab N, Friebe M, Illanes A. Cyclist Effort Features: A Novel Technique for Image Texture Characterization Applied to Larynx Cancer Classification in Contact Endoscopy-Narrow Band Imaging. Diagnostics (Basel) 2021; 11:432. [PMID: 33802625 PMCID: PMC8001098 DOI: 10.3390/diagnostics11030432] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 02/24/2021] [Accepted: 02/26/2021] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Feature extraction is an essential part of a Computer-Aided Diagnosis (CAD) system. It is usually preceded by a pre-processing step and followed by image classification. Usually, a large number of features is needed to end up with the desired classification results. In this work, we propose a novel approach for texture feature extraction. This method was tested on larynx Contact Endoscopy (CE)-Narrow Band Imaging (NBI) image classification to provide more objective information for otolaryngologists regarding the stage of the laryngeal cancer. METHODS The main idea of the proposed methods is to represent an image as a hilly surface, where different paths can be identified between a starting and an ending point. Each of these paths can be thought of as a Tour de France stage profile where a cyclist needs to perform a specific effort to arrive at the finish line. Several paths can be generated in an image where different cyclists produce an average cyclist effort representing important textural characteristics of the image. Energy and power as two Cyclist Effort Features (CyEfF) were extracted using this concept. The performance of the proposed features was evaluated for the classification of 2701 CE-NBI images into benign and malignant lesions using four supervised classifiers and subsequently compared with the performance of 24 Geometrical Features (GF) and 13 Entropy Features (EF). RESULTS The CyEfF features showed maximum classification accuracy of 0.882 and improved the GF classification accuracy by 3 to 12 percent. Moreover, CyEfF features were ranked as the top 10 features along with some features from GF set in two feature ranking methods. CONCLUSION The results prove that CyEfF with only two features can describe the textural characterization of CE-NBI images and can be part of the CAD system in combination with GF for laryngeal cancer diagnosis.
Collapse
Affiliation(s)
- Nazila Esmaeili
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (A.B.); (M.F.); (A.I.)
- Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University Munich, 85748 Munich, Germany;
| | - Axel Boese
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (A.B.); (M.F.); (A.I.)
| | - Nikolaos Davaris
- Department of Otorhinolaryngology, Head and Neck Surgery, Magdeburg University Hospital, 39120 Magdeburg, Germany; (N.D.); (C.A.)
| | - Christoph Arens
- Department of Otorhinolaryngology, Head and Neck Surgery, Magdeburg University Hospital, 39120 Magdeburg, Germany; (N.D.); (C.A.)
| | - Nassir Navab
- Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University Munich, 85748 Munich, Germany;
| | - Michael Friebe
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (A.B.); (M.F.); (A.I.)
- IDTM GmbH, 45657 Recklinghausen, Germany
| | - Alfredo Illanes
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (A.B.); (M.F.); (A.I.)
| |
Collapse
|
10
|
Osapoetra LO, Chan W, Tran W, Kolios MC, Czarnota GJ. Comparison of methods for texture analysis of QUS parametric images in the characterization of breast lesions. PLoS One 2020; 15:e0244965. [PMID: 33382837 PMCID: PMC7775053 DOI: 10.1371/journal.pone.0244965] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 12/18/2020] [Indexed: 01/06/2023] Open
Abstract
Purpose Accurate and timely diagnosis of breast carcinoma is very crucial because of its high incidence and high morbidity. Screening can improve overall prognosis by detecting the disease early. Biopsy remains as the gold standard for pathological confirmation of malignancy and tumour grading. The development of diagnostic imaging techniques as an alternative for the rapid and accurate characterization of breast masses is necessitated. Quantitative ultrasound (QUS) spectroscopy is a modality well suited for this purpose. This study was carried out to evaluate different texture analysis methods applied on QUS spectral parametric images for the characterization of breast lesions. Methods Parametric images of mid-band-fit (MBF), spectral-slope (SS), spectral-intercept (SI), average scatterer diameter (ASD), and average acoustic concentration (AAC) were determined using QUS spectroscopy from 193 patients with breast lesions. Texture methods were used to quantify heterogeneities of the parametric images. Three statistical-based approaches for texture analysis that include Gray Level Co-occurrence Matrix (GLCM), Gray Level Run-length Matrix (GRLM), and Gray Level Size Zone Matrix (GLSZM) methods were evaluated. QUS and texture-parameters were determined from both tumour core and a 5-mm tumour margin and were used in comparison to histopathological analysis in order to classify breast lesions as either benign or malignant. We developed a diagnostic model using different classification algorithms including linear discriminant analysis (LDA), k-nearest neighbours (KNN), support vector machine with radial basis function kernel (SVM-RBF), and an artificial neural network (ANN). Model performance was evaluated using leave-one-out cross-validation (LOOCV) and hold-out validation. Results Classifier performances ranged from 73% to 91% in terms of accuracy dependent on tumour margin inclusion and classifier methodology. Utilizing information from tumour core alone, the ANN achieved the best classification performance of 93% sensitivity, 88% specificity, 91% accuracy, 0.95 AUC using QUS parameters and their GLSZM texture features. Conclusions A QUS-based framework and texture analysis methods enabled classification of breast lesions with >90% accuracy. The results suggest that optimizing method for extracting discriminative textural features from QUS spectral parametric images can improve classification performance. Evaluation of the proposed technique on a larger cohort of patients with proper validation technique demonstrated the robustness and generalization of the approach.
Collapse
Affiliation(s)
- Laurentius O. Osapoetra
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - William Chan
- University of Waterloo, Toronto, Ontario, Canada
| | - William Tran
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
- Evaluative Clinical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | | | - Gregory J. Czarnota
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Physics, Ryerson University, Toronto, Ontario, Canada
- * E-mail:
| |
Collapse
|
11
|
Lee SH, Park H, Ko ES. Radiomics in Breast Imaging from Techniques to Clinical Applications: A Review. Korean J Radiol 2020; 21:779-792. [PMID: 32524780 PMCID: PMC7289696 DOI: 10.3348/kjr.2019.0855] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 01/31/2020] [Accepted: 02/28/2020] [Indexed: 02/06/2023] Open
Abstract
Recent advances in computer technology have generated a new area of research known as radiomics. Radiomics is defined as the high throughput extraction and analysis of quantitative features from imaging data. Radiomic features provide information on the gray-scale patterns, inter-pixel relationships, as well as shape and spectral properties of radiological images. Moreover, these features can be used to develop computational models that may serve as a tool for personalized diagnosis and treatment guidance. Although radiomics is becoming popular and widely used in oncology, many problems such as overfitting and reproducibility issues remain unresolved. In this review, we will outline the steps of radiomics used for oncology, specifically addressing applications for breast cancer patients and focusing on technical issues.
Collapse
Affiliation(s)
- Seung Hak Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea.,Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea.,School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Korea
| | - Eun Sook Ko
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
| |
Collapse
|
12
|
Osapoetra LO, Sannachi L, DiCenzo D, Quiaoit K, Fatima K, Czarnota GJ. Breast lesion characterization using Quantitative Ultrasound (QUS) and derivative texture methods. Transl Oncol 2020; 13:100827. [PMID: 32663657 PMCID: PMC7358267 DOI: 10.1016/j.tranon.2020.100827] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 06/12/2020] [Indexed: 12/19/2022] Open
Abstract
Purpose Accurate and timely diagnosis of breast cancer is extremely important because of its high incidence and high morbidity. Early diagnosis of breast cancer through screening can improve overall prognosis. Currently, biopsy remains as the gold standard for tumor pathological confirmation. Development of diagnostic imaging techniques for rapid and accurate characterization of breast lesions is required. We aim to evaluate the usefulness of texture-derivate features of QUS spectral parametric images for non-invasive characterization of breast lesions. Methods QUS Spectroscopy was used to determine parametric images of mid-band fit (MBF), spectral slope (SS), spectral intercept (SI), average scatterer diameter (ASD), and average acoustic concentration (AAC) in 204 patients with suspicious breast lesions. Subsequently, texture analysis techniques were used to generate texture maps from parametric images to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS parameters, texture-parameters and texture-derivate parameters were determined from both tumor core and a 5-mm tumor margin and were used in comparison to histopathological analysis in order to develop a diagnostic model for classifying breast lesions as either benign or malignant. Both leave-one-out and hold-out cross-validations were used to evaluate the performance of the diagnostic model. Three standard classification algorithms including a linear discriminant analysis (LDA), k-nearest neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated. Results Core and margin information using the SVM-RBF attained the best classification performance of 90% sensitivity, 92% specificity, 91% accuracy, and 0.93 AUC utilizing QUS parameters and their texture derivatives, evaluated using leave-one-out cross-validation. Implementation of hold-out cross-validation using combination of both core and margin information and SVM-RBF achieved average accuracy and AUC of 88% and 0.92, respectively. Conclusions QUS-based framework and derivative texture methods enable accurate classification of breast lesions. Evaluation of the proposed technique on a large cohort using hold-out cross-validation demonstrates its robustness and its generalization.
Collapse
Affiliation(s)
- Laurentius O Osapoetra
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada; Departments of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Lakshmanan Sannachi
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada; Departments of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Daniel DiCenzo
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Karina Quiaoit
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Kashuf Fatima
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Gregory J Czarnota
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada; Departments of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
| |
Collapse
|
13
|
Wang Y, Liao X, Xiao F, Zhang H, Li J, Liao M. Magnetic Resonance Imaging Texture Analysis in Differentiating Benign and Malignant Breast Lesions of Breast Imaging Reporting and Data System 4: A Preliminary Study. J Comput Assist Tomogr 2020; 44:83-89. [PMID: 31939887 DOI: 10.1097/rct.0000000000000969] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
RATIONALE AND OBJECTIVES This novel study aims to investigate texture parameters in distinguishing malignant and benign breast lesions classified as Breast Imaging Reporting and Data System 4 in dynamic contrast-enhanced magnetic resonance imaging (MRI). MATERIALS AND METHODS This retrospective study included 203 patients with 136 breast cancer and 67 benign lesions who underwent breast MRI between November 23, 2016, and August 27, 2018. Co-occurrence matrix-based texture features were extracted from each lesion on T1-weighted contrast-enhanced MRI using MatLab software. The association between texture parameters and breast lesions was analyzed, and the diagnostic model for breast cancer was created. Classification performance was evaluated by the area under the receiver operating characteristic curve, sensitivity, and specificity. RESULTS Significant differences were seen between malignant and benign lesions for a number of textural features, including contrast, correlation, autocorrelation, dissimilarity, cluster shade, and cluster performance (P < 0.05). After the analysis of the multicollinearity, 5 texture features (contrast, correlation, dissimilarity, cluster shade, and cluster performance) were included for the next principal component analysis. The differentiation accuracy of breast cancer based on the diagnostic model was 0.948 (95% confidence interval, 0.908-0.974). CONCLUSIONS Texture features that measure randomness, heterogeneity, or homogeneity may reflect underlying growth patterns of breast lesions and show great difference in malignant and benign lesions. Therefore, texture analysis may be a valuable assisted tool for diagnostic analysis on breast.
Collapse
Affiliation(s)
| | - Xing Liao
- Thyroid and Breast Surgery, ZhongNan Hospital of WuHan University, Wuchang District, Wuhan City, People's Republic of China
| | | | | | | | | |
Collapse
|
14
|
Discrimination Between Solitary Brain Metastasis and Glioblastoma Multiforme by Using ADC-Based Texture Analysis: A Comparison of Two Different ROI Placements. Acad Radiol 2019; 26:1466-1472. [PMID: 30770161 DOI: 10.1016/j.acra.2019.01.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 01/05/2019] [Accepted: 01/15/2019] [Indexed: 12/22/2022]
Abstract
RATIONALE AND OBJECTIVES To explore the value of texture analysis based on the apparent diffusion coefficient (ADC) value and the effect of region of interest (ROI) placements in distinguishing glioblastoma multiforme (GBM) from solitary brain metastasis (sMET). MATERIALS AND METHODS Sixty-two patients with pathologically confirmed GBM (n = 36) and sMET (n = 26) were retrospectively included. All patients underwent diffusion-weighted imaging with b values of 0 and 1000 s/mm2, and the ADC maps were generated automatically. ROIs were placed on the largest whole single-slice tumor (ROI1) and the enhanced solid portion (ROI2) of the ADC maps, respectively. The texture feature metrics of the histogram and gray-level co-occurrence matrix were then extracted by using in-house software. The parameters of the texture analysis were compared between GBM and sMET, using the Mann-Whitney U test. A receiver operating characteristic (ROC) curve analysis was performed to determine the best parameters for distinguishing between GBM from sMET. RESULTS Homogeneity and the inverse difference moment (IDM) of GBM were significantly higher than those of sMET in both ROIs (ROI1, p = 0.014 for homogeneity and p = 0.048 for IDM; ROI2, p< 0.001 for homogeneity and p = 0.029 for IDM). According to the ROC curve analysis, the area under the ROC curve (AUC) of homogeneity in ROI1 (AUC, 0.682, sensitivity, 72.2%, specificity, 61.5%) was significantly lower than that of ROI2 (AUC, 0.886, sensitivity, 83.3%, specificity, 76.9%; p= 0.012), whereas the IDM showed no statistical significance between two ROIs (p> 0.05). CONCLUSION The ADC-based texture analysis can help differentiate GBM from sMET, and the ROI on the solid portion would be recommended to calculate the ADC-based texture metrics.
Collapse
|
15
|
Nasief HG, Rosado-Mendez IM, Zagzebski JA, Hall TJ. A Quantitative Ultrasound-Based Multi-Parameter Classifier for Breast Masses. ULTRASOUND IN MEDICINE & BIOLOGY 2019; 45:1603-1616. [PMID: 31031035 PMCID: PMC7230148 DOI: 10.1016/j.ultrasmedbio.2019.02.025] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2018] [Revised: 02/21/2019] [Accepted: 02/28/2019] [Indexed: 05/31/2023]
Abstract
This manuscript reports preliminary results obtained by combining estimates of two or three (among seven) quantitative ultrasound (QUS) parameters in a model-free, multi-parameter classifier to differentiate breast carcinomas from fibroadenomas (the most common benign solid tumor). Forty-three patients scheduled for core biopsy of a suspicious breast mass were recruited. Radiofrequency echo signal data were acquired using clinical breast ultrasound systems equipped with linear array transducers. The reference phantom method was used to obtain system-independent estimates of the specific attenuation (ATT), the average backscatter coefficients, the effective scatterer diameter (ESD) and an effective scatterer diameter heterogeneity index (ESDHI) over regions of interest within each mass. In addition, the envelope amplitude signal-to-noise ratio (SNR), the Nakagami shape parameter, m, and the maximum collapsed average (maxCA) of the generalized spectrum were also computed. Classification was performed using the minimum Mahalanobis distance to the centroids of the training classes and tested against biopsy results. Classification performance was evaluated with the area under the receiver operating characteristic (ROC) curve. The best performance with a two-parameter classifier used the ESD and ESDHI and resulted in an area under the ROC curve of 0.98 (95% confidence interval [CI]: 0.95-1.00). Classification performance improved with three parameters (ATT, ESD and ESDHI) yielding an area under the ROC curve of 0.999 (0.995-1.000). These results suggest that system-independent QUS parameters, when combined in a model-free classifier, are a promising tool to characterize breast tumors. A larger study is needed to further test this idea.
Collapse
Affiliation(s)
- Haidy G Nasief
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Ivan M Rosado-Mendez
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA; Instituto de Fisica, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico
| | - James A Zagzebski
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Timothy J Hall
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA.
| |
Collapse
|
16
|
Perez-Moreno A, Dominguez M, Migliorelli F, Gratacos E, Palacio M, Bonet-Carne E. Clinical Feasibility of Quantitative Ultrasound Texture Analysis: A Robustness Study Using Fetal Lung Ultrasound Images. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2019; 38:1459-1476. [PMID: 30269384 DOI: 10.1002/jum.14824] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 08/20/2018] [Indexed: 06/08/2023]
Abstract
OBJECTIVES To compare the robustness of several methods based on quantitative ultrasound (US) texture analysis to evaluate its feasibility for extracting features from US images to use as a clinical diagnostic tool. METHODS We compared, ranked, and validated the robustness of 5 texture-based methods for extracting textural features from US images acquired under different conditions. For comparison and ranking purposes, we used 13,171 non-US images from widely known available databases (OUTEX [University of Oulu, Oulu, Finland] and PHOTEX [Texture Lab, Heriot-Watt University, Edinburgh, Scotland]), which were specifically acquired under different controlled parameters (illumination, resolution, and rotation) from 103 textures. The robustness of those methods with better results from the non-US images was validated by using 666 fetal lung US images acquired from singleton pregnancies. In this study, 2 similarity measurements (correlation and Chebyshev distances) were used to evaluate the repeatability of the features extracted from the same tissue images. RESULTS Three of the 5 methods (gray-level co-occurrence matrix, local binary patterns, and rotation-invariant local phase quantization) had favorably robust performance when using the non-US database. In fact, these methods showed similarity values close to 0 for the acquisition variations and delineations. Results from the US database confirmed robustness for all of the evaluated methods (gray-level co-occurrence matrix, local binary patterns, and rotation-invariant local phase quantization) when comparing the same texture obtained from different regions of the image (proximal/distal lungs and US machine brand stratification). CONCLUSIONS Our results confirmed that texture analysis can be robust (high similarity for different condition acquisitions) with potential to be included as a clinical tool.
Collapse
Affiliation(s)
- Alvaro Perez-Moreno
- Transmural Biotech SL, Barcelona, Spain
- Fetal I + D Fetal Medicine Research Center, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
| | | | - Federico Migliorelli
- Fetal I + D Fetal Medicine Research Center, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
- BCNatal, Barcelona Center for Maternal-Fetal and Neonatal Medicine, Hospital Clínic and Hospital Sant Joan de Deu, Barcelona, Spain
| | - Eduard Gratacos
- Fetal I + D Fetal Medicine Research Center, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
- BCNatal, Barcelona Center for Maternal-Fetal and Neonatal Medicine, Hospital Clínic and Hospital Sant Joan de Deu, Barcelona, Spain
- Center for Biomedical Research on Rare Diseases, Barcelona, Spain
| | - Montse Palacio
- Fetal I + D Fetal Medicine Research Center, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
- BCNatal, Barcelona Center for Maternal-Fetal and Neonatal Medicine, Hospital Clínic and Hospital Sant Joan de Deu, Barcelona, Spain
- Center for Biomedical Research on Rare Diseases, Barcelona, Spain
| | - Elisenda Bonet-Carne
- Transmural Biotech SL, Barcelona, Spain
- University College London, Microstructure Imaging Group, Center for Medical Image Computing, London, England
| |
Collapse
|
17
|
Breast tumor classification using different features of quantitative ultrasound parametric images. Int J Comput Assist Radiol Surg 2019; 14:623-633. [PMID: 30617720 DOI: 10.1007/s11548-018-01908-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 12/28/2018] [Indexed: 12/13/2022]
Abstract
RATIONALE AND OBJECTIVES The ultrasound B-mode-based morphological and texture analysis and Nakagami parametric imaging have been proposed to characterize breast tumors. Since these three feature categories of ultrasonic tissue characterization supply information on different physical characteristics of breast tumors, by combining the above methods is expected to provide more clues for classifying breast tumors. MATERIALS AND METHODS To verify the validity of the concept, raw data were obtained from 160 clinical cases. Six different types of morphological-feature parameters, four texture features, and the Nakagami parameter of benignancy and malignancy were extracted for evaluation. The Pearson's correlation matrix was used to calculate the correlation between different feature parameters. The fuzzy c-means clustering and stepwise regression techniques were utilized to determine the optimal feature set, respectively. The logistic regression, receiver operating characteristic curve, and support vector machine were used to estimate the diagnostic ability. RESULTS The best performance was obtained by combining morphological-feature parameter (e.g., standard deviation of the shortest distance), texture feature (e.g., variance), and the Nakagami parameter, with an accuracy of 89.4%, a specificity of 86.3%, a sensitivity of 92.5%, and an area under receiver operating characteristic curve of 0.96. There was no significant difference between using fuzzy c-means clustering, logistic regression, and support vector machine based on the optimal feature set for breast tumors classification. CONCLUSION Therefore, we verified that different physical ultrasonic features are functionally complementary and thus improve the performance in diagnosing breast tumors. Moreover, the optimal feature set had the maximum discriminating performance should be irrelative to the power of classifiers.
Collapse
|
18
|
Abstract
Precision medicine is increasingly pushed forward, also with respect to upcoming new targeted therapies. Individual characterization of diseases on the basis of biomarkers is a prerequisite for this development. So far, biomarkers are characterized clinically, histologically or on a molecular level. The implementation of broad screening methods (“Omics”) and the analysis of big data – in addition to single markers – allow to define biomarker signatures. Next to “Genomics”, “Proteomics”, and “Metabolicis”, “Radiomics” gained increasing interest during the last years. Based on radiologic imaging, multiple radiomic markers are extracted with the help of specific algorithms. These are correlated with clinical, (immuno-) histopathological, or genomic data. Underlying structural differences are based on the imaging metadata and are often not visible and therefore not detectable without specific software. Radiomics are depicted numerically or by graphs. The fact that radiomic information can be extracted from routinely performed imaging adds a specific appeal to this method. Radiomics could potentially replace biopsies and additional investigations. Alternatively, radiomics could complement other biomarkers and thus lead to a more precise, multimodal prediction. Until now, radiomics are primarily used to investigate solid tumors. Some promising studies in head and neck cancer have already been published.
Collapse
|
19
|
Brattain LJ, Telfer BA, Dhyani M, Grajo JR, Samir AE. Machine learning for medical ultrasound: status, methods, and future opportunities. Abdom Radiol (NY) 2018; 43:786-799. [PMID: 29492605 PMCID: PMC5886811 DOI: 10.1007/s00261-018-1517-0] [Citation(s) in RCA: 104] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.
Collapse
Affiliation(s)
| | - Brian A Telfer
- MIT Lincoln Laboratory, 244 Wood St, Lexington, MA, 02420, USA
| | - Manish Dhyani
- Department of Internal Medicine, Steward Carney Hospital, Boston, MA, 02124, USA
- Division of Ultrasound, Department of Radiology, Center for Ultrasound Research & Translation, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Joseph R Grajo
- Department of Radiology, Division of Abdominal Imaging, University of Florida College of Medicine, Gainesville, FL, USA
| | - Anthony E Samir
- Division of Ultrasound, Department of Radiology, Center for Ultrasound Research & Translation, Massachusetts General Hospital, Boston, MA, 02114, USA
| |
Collapse
|
20
|
Faust O, Acharya UR, Meiburger KM, Molinari F, Koh JE, Yeong CH, Kongmebhol P, Ng KH. Comparative assessment of texture features for the identification of cancer in ultrasound images: a review. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.01.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
21
|
柴 五, 杨 丰, 袁 绍, 梁 淑, 黄 靖. [A probability model for analyzing speckles in intravascular ultrasound images to facilitate image segmentation]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2017; 37:1476-1483. [PMID: 29180327 PMCID: PMC6779636 DOI: 10.3969/j.issn.1673-4254.2017.11.08] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Indexed: 06/07/2023]
Abstract
Ultrasonic image speckles result from the interference of the reflected signals by the scatters in the detected tissue. The physical characteristics of the speckles are closely correlated with the structures of the biological tissues, and the probability distribution of these speckles differs across different tissues. Based on the probability characteristics of intravascular ultrasound (IVUS) speckles, a Gamma mixture model and Gaussian mixture model are proposed to describe the calcified plaque, soft plaque and normal vascular regions on IVUS images. Using KS test, KL divergence and correlation coefficient analysis, we found that the probability distributions of the speckles generated by calcified plaques and normal blood vessels were better described by the Gaussian mixture model, while the speckles caused by soft plaques were described better by the Gamma mixture model. Based on this finding, we propose a probability mixture model combining neighborhood information for plaque segmentation on IVUS images. Compared with the existing probabilistic mixture model, the segmentation accuracy was greatly improved with a reduced noise.
Collapse
Affiliation(s)
- 五一 柴
- />南方医科大学生物医学工程学院广东省医学图像处理重点实验室,广东 广州 510515Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - 丰 杨
- />南方医科大学生物医学工程学院广东省医学图像处理重点实验室,广东 广州 510515Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - 绍锋 袁
- />南方医科大学生物医学工程学院广东省医学图像处理重点实验室,广东 广州 510515Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - 淑君 梁
- />南方医科大学生物医学工程学院广东省医学图像处理重点实验室,广东 广州 510515Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - 靖 黄
- />南方医科大学生物医学工程学院广东省医学图像处理重点实验室,广东 广州 510515Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| |
Collapse
|
22
|
Sadeghi-Naini A, Suraweera H, Tran WT, Hadizad F, Bruni G, Rastegar RF, Curpen B, Czarnota GJ. Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps. Sci Rep 2017; 7:13638. [PMID: 29057899 PMCID: PMC5651882 DOI: 10.1038/s41598-017-13977-x] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Accepted: 10/04/2017] [Indexed: 12/19/2022] Open
Abstract
This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofrequency data were acquired from 78 patients with suspicious breast lesions. QUS spectral-analysis techniques were performed on radiofrequency data to generate parametric maps of mid-band fit, spectral slope, spectral intercept, spacing among scatterers, average scatterer diameter, and average acoustic concentration. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps. These biomarkers were utilized to classify benign versus malignant lesions with leave-one-patient-out cross-validation. Results were compared to histopathology findings from biopsy specimens and radiology reports on MR images to evaluate the accuracy of technique. Among the biomarkers investigated, one mean-value parameter and 14 textural features demonstrated statistically significant differences (p < 0.05) between the two lesion types. A hybrid biomarker developed using a stepwise feature selection method could classify the legions with a sensitivity of 96%, a specificity of 84%, and an AUC of 0.97. Findings from this study pave the way towards adapting novel QUS-based frameworks for breast cancer screening and rapid diagnosis in clinic.
Collapse
Affiliation(s)
- Ali Sadeghi-Naini
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Harini Suraweera
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - William Tyler Tran
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Centre for Health and Social Care Research, Sheffield Hallam University, Sheffield, UK
| | - Farnoosh Hadizad
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Giancarlo Bruni
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | | | - Belinda Curpen
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Gregory J Czarnota
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada. .,Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada. .,Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada. .,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.
| |
Collapse
|
23
|
Matta TTD, Pereira WCDA, Radaelli R, Pinto RS, Oliveira LFD. Texture analysis of ultrasound images is a sensitive method to follow-up muscle damage induced by eccentric exercise. Clin Physiol Funct Imaging 2017; 38:477-482. [DOI: 10.1111/cpf.12441] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 04/27/2017] [Indexed: 12/13/2022]
Affiliation(s)
- Thiago Torres da Matta
- Universidade Federal do Rio de Janeiro - Programa de Engenharia Biomédica; Rio de Janeiro Brasil
- Universidade Federal do Rio de Janeiro - Escola de Educação Física e Desporto; Rio de Janeiro Brasil
| | | | - Regis Radaelli
- Universidade Federal do Rio Grande do Sul - Laboratório de Pesquisa e Exercício; Porto Alegre Brasil
| | - Ronei Silveira Pinto
- Universidade Federal do Rio Grande do Sul - Laboratório de Pesquisa e Exercício; Porto Alegre Brasil
| | - Liliam Fernandes de Oliveira
- Universidade Federal do Rio de Janeiro - Programa de Engenharia Biomédica; Rio de Janeiro Brasil
- Universidade Federal do Rio de Janeiro - Escola de Educação Física e Desporto; Rio de Janeiro Brasil
| |
Collapse
|
24
|
Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:7894705. [PMID: 28690670 PMCID: PMC5463197 DOI: 10.1155/2017/7894705] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Revised: 03/03/2017] [Accepted: 04/16/2017] [Indexed: 12/12/2022]
Abstract
We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL). After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag. The sparse and MIL problem is further converted to a conventional learning problem that is solved by relevance vector machine (RVM). Results of single classifiers are combined to be used for classification. Experimental results on the breast cancer datasets demonstrate the superiority of the proposed method in terms of classification accuracy as compared with state-of-the-art MIL methods.
Collapse
|
25
|
Banzato T, Fiore E, Morgante M, Manuali E, Zotti A. Texture analysis of B-mode ultrasound images to stage hepatic lipidosis in the dairy cow: A methodological study. Res Vet Sci 2016; 108:71-5. [PMID: 27663373 DOI: 10.1016/j.rvsc.2016.08.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Revised: 08/02/2016] [Accepted: 08/22/2016] [Indexed: 11/19/2022]
Abstract
Hepatic lipidosis is the most diffused hepatic disease in the lactating cow. A new methodology to estimate the degree of fatty infiltration of the liver in lactating cows by means of texture analysis of B-mode ultrasound images is proposed. B-mode ultrasonography of the liver was performed in 48 Holstein Friesian cows using standardized ultrasound parameters. Liver biopsies to determine the triacylglycerol content of the liver (TAGqa) were obtained from each animal. A large number of texture parameters were calculated on the ultrasound images by means of a free software. Based on the TAGqa content of the liver, 29 samples were classified as mild (TAGqa<50mg/g), 6 as moderate (50mg/g<TAGqa>100mg/g) and 13 as severe (TAG>100mg/g) in steatosis. Stepwise linear regression analysis was performed to predict the TAGqa content of the liver (TAGpred) from the texture parameters calculated on the ultrasound images. A five-variable model was used to predict the TAG content from the ultrasound images. The regression model explained 83.4% of the variance. An area under the curve (AUC) of 0.949 was calculated for <50mg/g vs >50mg/g of TAGqa; using an optimal cut-off value of 72mg/g TAGpred had a sensitivity of 86.2% and a specificity of 84.2%. An AUC of 0.978 for <100mg/g vs >100mg/g of TAGqa was calculated; using an optimal cut-off value of 89mg/g, TAGpred sensitivity was 92.3% and specificity was 88.6%. Texture analysis of B-mode ultrasound images may therefore be used to accurately predict the TAG content of the liver in lactating cows.
Collapse
Affiliation(s)
- Tommaso Banzato
- Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, Agripolis, 35020 Legnaro, Padua, Italy.
| | - Enrico Fiore
- Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, Agripolis, 35020 Legnaro, Padua, Italy.
| | - Massimo Morgante
- Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, Agripolis, 35020 Legnaro, Padua, Italy.
| | - Elisabetta Manuali
- Istituto Zooprofilattico Sperimentale dell'Umbria e delle Marche, Via G. Salvemini, 1, 06126 Perugia, Italy.
| | - Alessandro Zotti
- Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, Agripolis, 35020 Legnaro, Padua, Italy.
| |
Collapse
|
26
|
Parekh V, Jacobs MA. Radiomics: a new application from established techniques. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2016; 1:207-226. [PMID: 28042608 PMCID: PMC5193485 DOI: 10.1080/23808993.2016.1164013] [Citation(s) in RCA: 208] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The increasing use of biomarkers in cancer have led to the concept of personalized medicine for patients. Personalized medicine provides better diagnosis and treatment options available to clinicians. Radiological imaging techniques provide an opportunity to deliver unique data on different types of tissue. However, obtaining useful information from all radiological data is challenging in the era of "big data". Recent advances in computational power and the use of genomics have generated a new area of research termed Radiomics. Radiomics is defined as the high throughput extraction of quantitative imaging features or texture (radiomics) from imaging to decode tissue pathology and creating a high dimensional data set for feature extraction. Radiomic features provide information about the gray-scale patterns, inter-pixel relationships. In addition, shape and spectral properties can be extracted within the same regions of interest on radiological images. Moreover, these features can be further used to develop computational models using advanced machine learning algorithms that may serve as a tool for personalized diagnosis and treatment guidance.
Collapse
Affiliation(s)
- Vishwa Parekh
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging, The Johns Hopkins University School of Medicine, Baltimore, MD 21205
- Department of Computer Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21205
| | - Michael A. Jacobs
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging, The Johns Hopkins University School of Medicine, Baltimore, MD 21205
- Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD 21205
| |
Collapse
|
27
|
Zheng Y, Keller BM, Ray S, Wang Y, Conant EF, Gee JC, Kontos D. Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment. Med Phys 2016; 42:4149-60. [PMID: 26133615 DOI: 10.1118/1.4921996] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
PURPOSE Mammographic percent density (PD%) is known to be a strong risk factor for breast cancer. Recent studies also suggest that parenchymal texture features, which are more granular descriptors of the parenchymal pattern, can provide additional information about breast cancer risk. To date, most studies have measured mammographic texture within selected regions of interest (ROIs) in the breast, which cannot adequately capture the complexity of the parenchymal pattern throughout the whole breast. To better characterize patterns of the parenchymal tissue, the authors have developed a fully automated software pipeline based on a novel lattice-based strategy to extract a range of parenchymal texture features from the entire breast region. METHODS Digital mammograms from 106 cases with 318 age-matched controls were retrospectively analyzed. The lattice-based approach is based on a regular grid virtually overlaid on each mammographic image. Texture features are computed from the intersection (i.e., lattice) points of the grid lines within the breast, using a local window centered at each lattice point. Using this strategy, a range of statistical (gray-level histogram, co-occurrence, and run-length) and structural (edge-enhancing, local binary pattern, and fractal dimension) features are extracted. To cover the entire breast, the size of the local window for feature extraction is set equal to the lattice grid spacing and optimized experimentally by evaluating different windows sizes. The association between their lattice-based texture features and breast cancer was evaluated using logistic regression with leave-one-out cross validation and further compared to that of breast PD% and commonly used single-ROI texture features extracted from the retroareolar or the central breast region. Classification performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC). DeLong's test was used to compare the different ROCs in terms of AUC performance. RESULTS The average univariate performance of the lattice-based features is higher when extracted from smaller than larger window sizes. While not every individual texture feature is superior to breast PD% (AUC: 0.59, STD: 0.03), their combination in multivariate analysis has significantly better performance (AUC: 0.85, STD: 0.02, p < 0.001). The lattice-based texture features also outperform the single-ROI texture features when extracted from the retroareolar or the central breast region (AUC: 0.60-0.74, STD: 0.03). Adding breast PD% does not make a significant performance improvement to the lattice-based texture features or the single-ROI features (p > 0.05). CONCLUSIONS The proposed lattice-based strategy for mammographic texture analysis enables to characterize the parenchymal pattern over the entire breast. As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment. Larger studies are warranted to validate these findings and also compare to standard demographic and reproductive risk factors.
Collapse
Affiliation(s)
- Yuanjie Zheng
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market Street, Suite 370, Philadelphia, Pennsylvania 19104
| | - Brad M Keller
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market Street, Suite 370, Philadelphia, Pennsylvania 19104
| | - Shonket Ray
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market Street, Suite 370, Philadelphia, Pennsylvania 19104
| | - Yan Wang
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market Street, Suite 370, Philadelphia, Pennsylvania 19104
| | - Emily F Conant
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market Street, Suite 370, Philadelphia, Pennsylvania 19104
| | - James C Gee
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market Street, Suite 370, Philadelphia, Pennsylvania 19104
| | - Despina Kontos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market Street, Suite 370, Philadelphia, Pennsylvania 19104
| |
Collapse
|
28
|
Song G, Xue F, Zhang C. A Model Using Texture Features to Differentiate the Nature of Thyroid Nodules on Sonography. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2015; 34:1753-1760. [PMID: 26307120 DOI: 10.7863/ultra.15.14.10045] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2014] [Accepted: 12/28/2014] [Indexed: 06/04/2023]
Abstract
OBJECTIVES To evaluate the use of texture-based gray-level co-occurrence matrix (GLCM) features extracted from thyroid sonograms in building prediction models to determine the nature of thyroid nodules. METHODS A GLCM was used to extract the texture features of 155 sonograms of thyroid nodules (76 benign and 79 malignant). The GLCM features included energy, contrast, correlation, sum of squares, inverse difference moment, sum average, sum variance, sum entropy, entropy, difference variance, difference entropy, information measures of correlation, and maximal correlation coefficient. The texture features extracted by the GLCM were used to build 6 different statistical models, including support vector machine, random tree, random forest, boost, logistic, and artificial neural network models. The models' performances were evaluated by 10-fold cross-validation combining a receiver operating characteristic curve, indices of accuracy, true-positive rate, false-positive rate, sensitivity, specificity, precision, recall, F-measure, and area under the receiver operating characteristic curve. External validation was used to examine the stability of the model that showed the best performance. RESULTS The logistic model showed the best performance, according to 10-fold cross-validation, among the 6 models, with the highest area under the curve (0.84), accuracy (78.5%), true-positive rate (0.785), sensitivity (0.789), specificity (0.785), precision (0.789), recall (0.785), and F-measure (0.784), as well as the lowest false-positive rate (0.215). The external validation results showed that the logistic model was stable. CONCLUSIONS Gray-level co-occurrence matrix texture features extracted from sonograms of thyroid nodules coupled with a logistic model are useful for differentiating between benign and malignant thyroid nodules.
Collapse
Affiliation(s)
- Gesheng Song
- School of Medicine (G.S.), and Department of Epidemiology and Biostatistics, School of Public Health (F.X.), Shandong University, Jinan, China; and Health Management Center, Shandong Provincial Qianfoshan Hospital, Jinan, China (C.Z.)
| | - Fuzhong Xue
- School of Medicine (G.S.), and Department of Epidemiology and Biostatistics, School of Public Health (F.X.), Shandong University, Jinan, China; and Health Management Center, Shandong Provincial Qianfoshan Hospital, Jinan, China (C.Z.)
| | - Chengqi Zhang
- School of Medicine (G.S.), and Department of Epidemiology and Biostatistics, School of Public Health (F.X.), Shandong University, Jinan, China; and Health Management Center, Shandong Provincial Qianfoshan Hospital, Jinan, China (C.Z.).
| |
Collapse
|
29
|
Chen X, Wei X, Zhang Z, Yang R, Zhu Y, Jiang X. Differentiation of true-progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide by GLCM texture analysis of conventional MRI. Clin Imaging 2015; 39:775-80. [DOI: 10.1016/j.clinimag.2015.04.003] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2015] [Revised: 03/20/2015] [Accepted: 04/06/2015] [Indexed: 11/28/2022]
|
30
|
Cai L, Wang X, Wang Y, Guo Y, Yu J, Wang Y. Robust phase-based texture descriptor for classification of breast ultrasound images. Biomed Eng Online 2015; 14:26. [PMID: 25889570 PMCID: PMC4376500 DOI: 10.1186/s12938-015-0022-8] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2014] [Accepted: 03/05/2015] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Classification of breast ultrasound (BUS) images is an important step in the computer-aided diagnosis (CAD) system for breast cancer. In this paper, a novel phase-based texture descriptor is proposed for efficient and robust classifiers to discriminate benign and malignant tumors in BUS images. METHOD The proposed descriptor, namely the phased congruency-based binary pattern (PCBP) is an oriented local texture descriptor that combines the phase congruency (PC) approach with the local binary pattern (LBP). The support vector machine (SVM) is further applied for the tumor classification. To verify the efficiency of the proposed PCBP texture descriptor, we compare the PCBP with other three state-of-art texture descriptors, and experiments are carried out on a BUS image database including 138 cases. The receiver operating characteristic (ROC) analysis is firstly performed and seven criteria are utilized to evaluate the classification performance using different texture descriptors. Then, in order to verify the robustness of the PCBP against illumination variations, we train the SVM classifier on texture features obtained from the original BUS images, and use this classifier to deal with the texture features extracted from BUS images with different illumination conditions (i.e., contrast-improved, gamma-corrected and histogram-equalized). The area under ROC curve (AUC) index is used as the figure of merit to evaluate the classification performances. RESULTS AND CONCLUSIONS The proposed PCBP texture descriptor achieves the highest values (i.e. 0.894) and the least variations in respect of the AUC index, regardless of the gray-scale variations. It's revealed in the experimental results that classifications of BUS images with the proposed PCBP texture descriptor are efficient and robust, which may be potentially useful for breast ultrasound CADs.
Collapse
Affiliation(s)
- Lingyun Cai
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China.
| | - Xin Wang
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China.
| | - Yuanyuan Wang
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China. .,Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Shanghai, 200433, China.
| | - Yi Guo
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China. .,Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Shanghai, 200433, China.
| | - Jinhua Yu
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China. .,Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Shanghai, 200433, China.
| | - Yi Wang
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, 200040, China.
| |
Collapse
|
31
|
Sikiö M, Kölhi P, Ryymin P, Eskola HJ, Dastidar P. MRI Texture Analysis and Diffusion Tensor Imaging in Chronic Right Hemisphere Ischemic Stroke. J Neuroimaging 2014; 25:614-9. [PMID: 25482992 DOI: 10.1111/jon.12185] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2014] [Revised: 07/30/2014] [Accepted: 08/16/2014] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND AND PURPOSE Diffusion tensor imaging (DTI) is shown to reveal changes caused by cerebral infarction. The aim of this study is to reveal those changes also in the conventional magnetic resonance (MR) images using a quantitative image analysis method, texture analysis (TA). METHODS Thirty patients who had suffered their first ever infarction located on the right hemisphere underwent DTI and conventional MRI studies in the chronic phase. DTI parameters fractional anisotropy and mean diffusivity, as well as four second-order texture parameters were calculated. Interhemispheric differences and correlations between DTI and TA parameters were evaluated. RESULTS Our DTI findings supported earlier studies as fractional anisotropy values were lowered and mean diffusivity values elevated in the lesion site, and ipsilateral cerebral peduncle, thalamus, and centrum semiovale compared to the unaffected side. Textural homogeneity parameters showed lower and complexity parameters higher values in the lesion site and ipsilateral centrum semiovale compared to the contralateral hemisphere. Correlation between the two methods was found in ipsilateral mesencephalon. CONCLUSIONS In addition to DTI method, TA could assist in revealing the changes caused by infarction, also outside the lesion site. Damaged areas were found more heterogeneous and random in texture compared to unaffected sites.
Collapse
Affiliation(s)
- Minna Sikiö
- Department of Radiology, Medical Imaging Center and Hospital Pharmacy, Tampere University Hospital, Tampere, Finland.,Department of Electronics and Communications Engineering, Tampere University of Technology, Tampere, Finland
| | - Paula Kölhi
- Department of Radiology, Medical Imaging Center and Hospital Pharmacy, Tampere University Hospital, Tampere, Finland.,Department of Electronics and Communications Engineering, Tampere University of Technology, Tampere, Finland
| | - Pertti Ryymin
- Department of Radiology, Medical Imaging Center and Hospital Pharmacy, Tampere University Hospital, Tampere, Finland
| | - Hannu J Eskola
- Department of Radiology, Medical Imaging Center and Hospital Pharmacy, Tampere University Hospital, Tampere, Finland.,Department of Electronics and Communications Engineering, Tampere University of Technology, Tampere, Finland
| | - Prasun Dastidar
- Department of Radiology, Medical Imaging Center and Hospital Pharmacy, Tampere University Hospital, Tampere, Finland.,Medical School, University of Tampere, Tampere, Finland
| |
Collapse
|
32
|
Alic L, Niessen WJ, Veenland JF. Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review. PLoS One 2014; 9:e110300. [PMID: 25330171 PMCID: PMC4203782 DOI: 10.1371/journal.pone.0110300] [Citation(s) in RCA: 110] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Accepted: 09/15/2014] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Many techniques are proposed for the quantification of tumor heterogeneity as an imaging biomarker for differentiation between tumor types, tumor grading, response monitoring and outcome prediction. However, in clinical practice these methods are barely used. This study evaluates the reported performance of the described methods and identifies barriers to their implementation in clinical practice. METHODOLOGY The Ovid, Embase, and Cochrane Central databases were searched up to 20 September 2013. Heterogeneity analysis methods were classified into four categories, i.e., non-spatial methods (NSM), spatial grey level methods (SGLM), fractal analysis (FA) methods, and filters and transforms (F&T). The performance of the different methods was compared. PRINCIPAL FINDINGS Of the 7351 potentially relevant publications, 209 were included. Of these studies, 58% reported the use of NSM, 49% SGLM, 10% FA, and 28% F&T. Differentiation between tumor types, tumor grading and/or outcome prediction was the goal in 87% of the studies. Overall, the reported area under the curve (AUC) ranged from 0.5 to 1 (median 0.87). No relation was found between the performance and the quantification methods used, or between the performance and the imaging modality. A negative correlation was found between the tumor-feature ratio and the AUC, which is presumably caused by overfitting in small datasets. Cross-validation was reported in 63% of the classification studies. Retrospective analyses were conducted in 57% of the studies without a clear description. CONCLUSIONS In a research setting, heterogeneity quantification methods can differentiate between tumor types, grade tumors, and predict outcome and monitor treatment effects. To translate these methods to clinical practice, more prospective studies are required that use external datasets for validation: these datasets should be made available to the community to facilitate the development of new and improved methods.
Collapse
Affiliation(s)
- Lejla Alic
- Biomedical Imaging Group Rotterdam, Department of Radiology and Medical Informatics, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Intelligent Imaging, Netherlands Organization for Applied Scientific Research (TNO), The Hague, The Netherlands
| | - Wiro J. Niessen
- Biomedical Imaging Group Rotterdam, Department of Radiology and Medical Informatics, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
- Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - Jifke F. Veenland
- Biomedical Imaging Group Rotterdam, Department of Radiology and Medical Informatics, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
| |
Collapse
|
33
|
Damerjian V, Tankyevych O, Souag N, Petit E. Speckle characterization methods in ultrasound images – A review. Ing Rech Biomed 2014. [DOI: 10.1016/j.irbm.2014.05.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
34
|
Parikh J, Selmi M, Charles-Edwards G, Glendenning J, Ganeshan B, Verma H, Mansi J, Harries M, Tutt A, Goh V. Changes in primary breast cancer heterogeneity may augment midtreatment MR imaging assessment of response to neoadjuvant chemotherapy. Radiology 2014; 272:100-12. [PMID: 24654970 DOI: 10.1148/radiol.14130569] [Citation(s) in RCA: 98] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
PURPOSE To evaluate whether changes in magnetic resonance (MR) imaging heterogeneity may aid assessment for pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT) in primary breast cancer and to compare pCR with standard Response Evaluation Criteria in Solid Tumors response. MATERIALS AND METHODS Institutional review board approval, with waiver of informed consent, was obtained for this retrospective analysis of 36 consecutive female patients, with unilateral unifocal primary breast cancer larger than 2 cm in diameter who were receiving sequential anthracycline-taxane NACT between October 2008 and October 2012. T2- and T1-weighted dynamic contrast material-enhanced MR imaging was performed before, at midtreatment (after three cycles), and after NACT. Changes in tumor entropy (irregularity) and uniformity (gray-level distribution) were determined before and after MR image filtration (for different-sized features). Entropy and uniformity for pathologic complete responders and nonresponders were compared by using the Mann-Whitney U test and receiver operating characteristic analysis. RESULTS With NACT, there was an increase in uniformity and a decrease in entropy on T2-weighted and contrast-enhanced subtracted T1-weighted MR images for all filters (uniformity: 23.45% and 22.62%; entropy: -19.15% and -19.26%, respectively). There were eight complete pathologic responders. An area under the curve of 0.84 for T2-weighted MR imaging entropy and uniformity (P = .004 and .003) and 0.66 for size (P = .183) for pCR was found, giving a sensitivity and specificity of 87.5% and 82.1% for entropy and 87.5% and 78.6% for uniformity compared with 50% and 82.1%, respectively, for tumor size change for association with pCR. CONCLUSION Tumors become more homogeneous with treatment. An increase in T2-weighted MR imaging uniformity and a decrease in T2-weighted MR imaging entropy following NACT may provide an earlier indication of pCR than tumor size change.
Collapse
Affiliation(s)
- Jyoti Parikh
- From the Departments of Radiology (J.P., H.V., V.G.), Clinical Oncology (J.G., A.T.), and Medical Oncology (J.M., M.H.), Guys and St Thomas' Hospitals NHS Foundation Trust, Westminster Bridge Road, London SE1 7EH, England; Division of Imaging Sciences and Biomedical Engineering, King's College, London, England (M.S., G.C., V.G.); and Institute of Nuclear Medicine, University College London, London, England (B.G.)
| | | | | | | | | | | | | | | | | | | |
Collapse
|
35
|
Xu W, Liu Y, Lu Z, Jin ZD, Hu YH, Yu JG, Li ZS. A new endoscopic ultrasonography image processing method to evaluate the prognosis for pancreatic cancer treated with interstitial brachytherapy. World J Gastroenterol 2013; 19:6479-6484. [PMID: 24151368 PMCID: PMC3798413 DOI: 10.3748/wjg.v19.i38.6479] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2013] [Revised: 08/28/2013] [Accepted: 09/05/2013] [Indexed: 02/06/2023] Open
Abstract
AIM: To develop a fuzzy classification method to score the texture features of pancreatic cancer in endoscopic ultrasonography (EUS) images and evaluate its utility in making prognosis judgments for patients with unresectable pancreatic cancer treated by EUS-guided interstitial brachytherapy.
METHODS: EUS images from our retrospective database were analyzed. The regions of interest were drawn, and texture features were extracted, selected, and scored with a fuzzy classification method using a C++ program. Then, patients with unresectable pancreatic cancer were enrolled to receive EUS-guided iodine 125 radioactive seed implantation. Their fuzzy classification scores, tumor volumes, and carbohydrate antigen 199 (CA199) levels before and after the brachytherapy were recorded. The association between the changes in these parameters and overall survival was analyzed statistically.
RESULTS: EUS images of 153 patients with pancreatic cancer and 63 non-cancer patients were analyzed. A total of 25 consecutive patients were enrolled, and they tolerated the brachytherapy well without any complications. There was a correlation between the change in the fuzzy classification score and overall survival (Spearman test, r = 0.616, P = 0.001), whereas no correlation was found to be significant between the change in tumor volume (P = 0.663), CA199 level (P = 0.659), and overall survival. There were 15 patients with a decrease in their fuzzy classification score after brachytherapy, whereas the fuzzy classification score increased in another 10 patients. There was a significant difference in overall survival between the two groups (67 d vs 151 d, P = 0.001), but not in the change of tumor volume and CA199 level.
CONCLUSION: Using the fuzzy classification method to analyze EUS images of pancreatic cancer is feasible, and the method can be used to make prognosis judgments for patients with unresectable pancreatic cancer treated by interstitial brachytherapy.
Collapse
|
36
|
Differentiation of pancreatic cancer and chronic pancreatitis using computer-aided diagnosis of endoscopic ultrasound (EUS) images: a diagnostic test. PLoS One 2013; 8:e63820. [PMID: 23704940 PMCID: PMC3660382 DOI: 10.1371/journal.pone.0063820] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2012] [Accepted: 04/08/2013] [Indexed: 02/07/2023] Open
Abstract
Background Differentiating pancreatic cancer (PC) from normal tissue by computer-aided diagnosis of EUS images were quite useful. The current study was designed to investigate the feasibility of using computer-aided diagnostic (CAD) techniques to extract EUS image parameters for the differential diagnosis of PC and chronic pancreatitis (CP). Methodology/Principal Findings This study recruited 262 patients with PC and 126 patients with CP. Typical EUS images were selected from the sample sets. Texture features were extracted from the region of interest using computer-based techniques. Then the distance between class algorithm and sequential forward selection (SFS) algorithm were used for a better combination of features; and, later, a support vector machine (SVM) predictive model was built, trained, and validated. Overall, 105 features of 9 categories were extracted from the EUS images for pattern classification. Of these features, the 16 were selected as a better combination of features. Then, SVM predictive model was built and trained. The total cases were randomly divided into a training set and a testing set. The training set was used to train the SVM, and the testing set was used to evaluate the performance of the SVM. After 200 trials of randomised experiments, the average accuracy, sensitivity, specificity, the positive and negative predictive values of pancreatic cancer were 94.2±0.1749%,96.25±0.4460%, 93.38±0.2076%, 92.21±0.4249% and 96.68±0.1471%, respectively. Conclusions/Significance Digital image processing and computer-aided EUS image differentiation technologies are highly accurate and non-invasive. This technology provides a kind of new and valuable diagnostic tool for the clinical determination of PC.
Collapse
|
37
|
Song CI, Ryu CH, Choi SH, Roh JL, Nam SY, Kim SY. Quantitative evaluation of vocal-fold mucosal irregularities using GLCM-based texture analysis. Laryngoscope 2013; 123:E45-50. [DOI: 10.1002/lary.24151] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2012] [Revised: 01/11/2013] [Accepted: 03/22/2013] [Indexed: 11/08/2022]
Affiliation(s)
- Chan Il Song
- Department of Otolaryngology; Asan Medical Center, College of Medicine, University of Ulsan; Seoul Korea
| | - Chang Hwan Ryu
- Head and Neck Oncology clinic; Center for Specific Organ Center, Research Institute and Hospital, National Cancer Center; Goyang-si Gyeonggi-do Korea
| | - Seung-Ho Choi
- Department of Otolaryngology; Asan Medical Center, College of Medicine, University of Ulsan; Seoul Korea
| | - Jong-Lyel Roh
- Department of Otolaryngology; Asan Medical Center, College of Medicine, University of Ulsan; Seoul Korea
| | - Soon Yuhl Nam
- Department of Otolaryngology; Asan Medical Center, College of Medicine, University of Ulsan; Seoul Korea
| | - Sang Yoon Kim
- Department of Otolaryngology; Asan Medical Center, College of Medicine, University of Ulsan; Seoul Korea
| |
Collapse
|
38
|
Giger ML, Karssemeijer N, Schnabel JA. Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annu Rev Biomed Eng 2013; 15:327-57. [PMID: 23683087 DOI: 10.1146/annurev-bioeng-071812-152416] [Citation(s) in RCA: 117] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The role of breast image analysis in radiologists' interpretation tasks in cancer risk assessment, detection, diagnosis, and treatment continues to expand. Breast image analysis methods include segmentation, feature extraction techniques, classifier design, biomechanical modeling, image registration, motion correction, and rigorous methods of evaluation. We present a review of the current status of these task-based image analysis methods, which are being developed for the various image acquisition modalities of mammography, tomosynthesis, computed tomography, ultrasound, and magnetic resonance imaging. Depending on the task, image-based biomarkers from such quantitative image analysis may include morphological, textural, and kinetic characteristics and may depend on accurate modeling and registration of the breast images. We conclude with a discussion of future directions.
Collapse
Affiliation(s)
- Maryellen L Giger
- Department of Radiology, University of Chicago, Chicago, IL 60637, USA.
| | | | | |
Collapse
|
39
|
Ding J, Cheng HD, Huang J, Liu J, Zhang Y. Breast ultrasound image classification based on multiple-instance learning. J Digit Imaging 2013; 25:620-7. [PMID: 22733258 DOI: 10.1007/s10278-012-9499-x] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Breast ultrasound (BUS) image segmentation is a very difficult task due to poor image quality and speckle noise. In this paper, local features extracted from roughly segmented regions of interest (ROIs) are used to describe breast tumors. The roughly segmented ROI is viewed as a bag. And subregions of the ROI are considered as the instances of the bag. Multiple-instance learning (MIL) method is more suitable for classifying breast tumors using BUS images. However, due to the complexity of BUS images, traditional MIL method is not applicable. In this paper, a novel MIL method is proposed for solving such task. First, a self-organizing map is used to map the instance space to the concept space. Then, we use the distribution of the instances of each bag in the concept space to construct the bag feature vector. Finally, a support vector machine is employed for classifying the tumors. The experimental results show that the proposed method can achieve better performance: the accuracy is 0.9107 and the area under receiver operator characteristic curve is 0.96 (p < 0.005).
Collapse
Affiliation(s)
- Jianrui Ding
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, No. 92, Xidazhi Street, Harbin, 150001, People's Republic of China
| | | | | | | | | |
Collapse
|
40
|
Liao YY, Wu JC, Li CH, Yeh CK. Texture feature analysis for breast ultrasound image enhancement. ULTRASONIC IMAGING 2011; 33:264-278. [PMID: 22518956 DOI: 10.1177/016173461103300405] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Texture analysis of breast ultrasound B-scans has been widely applied to the segmentation and classification of breast tumors. We present a parametric imaging method based on the texture features to preserve tumor edges and retain the texture information simultaneously. Four texture-feature parameters--homogeneity, contrast, energy and variance--were evaluated using the gray-level co-occurrence matrix. The local texture-feature parameter was assigned as the new pixel located at the center of the sliding window at each position. This process yielded the texture-feature parametric image as the map of texture-feature values. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were estimated to show the quality improvement of the images. The contours outlined from 11 experienced physicians and the gradient vector flow (GVF) snake algorithm segmentations were adopted to verify the edge enhancement of texture-feature parametric images. In addition, the Fisher's linear discriminant analysis (FLDA) and receiver-operating-characteristic (ROC) curve were used to test the performance of breast tumor classifications between texture-feature parametric images and B-scan images. The results show that the variance images have higher CNR and SNR estimates than those in the B-scan images. There was a high agreement between the physician's manual contours and the GVF snake automatic segmentations in the variance images, and the mean area overlap was over 93%. The area under the ROC curve from the B-scan images had 0.81 and 95% confidence interval of 0.72-0.88, and the texture-feature parametric images had 0.90 and 95% confidence interval of 0.84-0.96. These findings indicate that the texture-feature parametric imaging method can be not only useful for determining the location of the lesion boundary but also as a tool to improve the accuracy of breast tumor classifications.
Collapse
Affiliation(s)
- Yin-Yin Liao
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan
| | | | | | | |
Collapse
|
41
|
Su Y, Wang Y, Jiao J, Guo Y. Automatic detection and classification of breast tumors in ultrasonic images using texture and morphological features. Open Med Inform J 2011; 5:26-37. [PMID: 21892371 PMCID: PMC3158436 DOI: 10.2174/1874431101105010026] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2011] [Revised: 05/15/2011] [Accepted: 05/15/2011] [Indexed: 11/22/2022] Open
Abstract
Due to severe presence of speckle noise, poor image contrast and irregular lesion shape, it is challenging to build a fully automatic detection and classification system for breast ultrasonic images. In this paper, a novel and effective computer-aided method including generation of a region of interest (ROI), segmentation and classification of breast tumor is proposed without any manual intervention. By incorporating local features of texture and position, a ROI is firstly detected using a self-organizing map neural network. Then a modified Normalized Cut approach considering the weighted neighborhood gray values is proposed to partition the ROI into clusters and get the initial boundary. In addition, a regional-fitting active contour model is used to adjust the few inaccurate initial boundaries for the final segmentation. Finally, three textures and five morphologic features are extracted from each breast tumor; whereby a highly efficient Affinity Propagation clustering is used to fulfill the malignancy and benign classification for an existing database without any training process. The proposed system is validated by 132 cases (67 benignancies and 65 malignancies) with its performance compared to traditional methods such as level set segmentation, artificial neural network classifiers, and so forth. Experiment results show that the proposed system, which needs no training procedure or manual interference, performs best in detection and classification of ultrasonic breast tumors, while having the lowest computation complexity.
Collapse
Affiliation(s)
- Yanni Su
- Department of Electronic Engineering, Fudan University, Shanghai 200433, China
| | | | | | | |
Collapse
|
42
|
Chen DR, Lai HW. Three-dimensional ultrasonography for breast malignancy detection. ACTA ACUST UNITED AC 2011; 5:253-61. [PMID: 23484500 DOI: 10.1517/17530059.2011.561314] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
INTRODUCTION Breast ultrasound is used not only to differentiate a solid breast mass from a cyst and to assist in guided biopsy, but also to classify benign and malignant lesions, with good resolution gray-scale imaging equipped with color Doppler adequate for daily clinical practice in most circumstances. AREAS COVERED This article critically reviews three-dimensional (3D) ultrasound for the detection of breast malignancies in comparison with the popular two-dimensional ultrasound, highlighting the advantages it has over other imaging modalities as well as the drawbacks that are presented. In particular, the article looks at how 3D ultrasound planes help us to define more clearly the margins, that is, microlobulation and papillomas, of breast tumors. This paper also highlights how the resolution and multiple planes of 3D ultrasound can clearly demonstrate skin tumor infiltration for evaluation and how it can be used for planning, monitoring and treatment of breast cancer. EXPERT OPINION As with any new technology, 3D ultrasound has a learning curve and clinicians will need to master the technology in order to use this tool to its full potential. Although 3D ultrasound does have its limitations, a better understanding of its settings along with the optimization of image acquisition and a better ability to manipulate data during analysis will lead to 3D ultrasound becoming a useful tool for breast malignancy detection.
Collapse
Affiliation(s)
- Dar-Ren Chen
- Changhua Christian Hospital, Comprehensive Breast Cancer Center, 135 Nanhsiao Street, Changhua 500 , Taiwan +886 4 723 8595 ext. 4871 ; +886 4 723 3715 ;
| | | |
Collapse
|
43
|
Alam SK, Feleppa EJ, Rondeau M, Kalisz A, Garra BS. Ultrasonic multi-feature analysis procedure for computer-aided diagnosis of solid breast lesions. ULTRASONIC IMAGING 2011; 33:17-38. [PMID: 21608446 DOI: 10.1177/016173461103300102] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
We have developed quantitative descriptors to provide an objective means of noninvasive identification of cancerous breast lesions. These descriptors include quantitative acoustic features assessed using spectrum analysis of ultrasonic radiofrequency (rf) echo signals and morphometric properties related to lesion shape. Acoustic features include measures of echogenicity, heterogeneity and shadowing, computed by generating spectral-parameter images of the lesion and surrounding tissue. Spectral-parameter values are derived from rf echo signals at each pixel using a sliding-window Fourier analysis. We derive quantitative acoustic features from spectral-parameter maps of the lesion and adjacent areas. We quantify morphometric features by geometric and fractal analysis of traced lesion boundaries. Initial results on biopsy-proven cases show that although a single parameter cannot reliably discriminate cancerous from noncancerous breast lesions, multi-feature analysis provides excellent discrimination for this data set. We have processed data for 130 biopsy-proven patients, acquired during routine ultrasonic examinations at three clinical sites and produced an area under the receiver-operating-characteristics (ROC) curve of 0.947 +/- 0.045. Among the quantitative descriptors, lesion-margin definition, spiculation and border irregularity are the most useful; some additional morphometric features (such as border irregularity) also are particularly effective in lesion classification. Our findings are consistent with many of the BI-RADS (Breast Imaging Reporting and Data System) breast-lesion-classification criteria in use today.
Collapse
Affiliation(s)
- S Kaisar Alam
- Riverside Research, 156 William Street, New York, NY 10038, USA.
| | | | | | | | | |
Collapse
|
44
|
Yuan Y, Giger ML, Li H, Bhooshan N, Sennett CA. Multimodality computer-aided breast cancer diagnosis with FFDM and DCE-MRI. Acad Radiol 2010; 17:1158-67. [PMID: 20692620 PMCID: PMC4634529 DOI: 10.1016/j.acra.2010.04.015] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2010] [Revised: 04/09/2010] [Accepted: 04/26/2010] [Indexed: 12/13/2022]
Abstract
RATIONALE AND OBJECTIVES To investigate a multimodality computer-aided diagnosis (CAD) scheme that combines image information from full-field digital mammography (FFDM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for computerized breast cancer classification. MATERIALS AND METHODS From a retrospective FFDM database with 432 lesions (255 malignant, 177 benign) and a retrospective DCE-MRI database including 476 lesions (347 malignant, 129 benign), we constructed a multimodality dataset of 213 lesions (168 malignant, 45 benign). Each lesion was present on both FFDM and DCE-MRI images and deemed to be a difficult case given the necessity of having both clinical imaging exams. Using a manually indicated lesion location (ie, a seed point on FFDM images or a region of interest on DCE-MRI images, the computer automatically segmented the mass lesions and extracted lesion features). A subset of features was selected using linear stepwise feature selection and merged by a Bayesian artificial neural network to yield an estimate of the probability of malignancy. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the selected features in distinguishing between malignant and benign lesions. RESULTS With leave-one-lesion-out cross-validation on the multimodality dataset, the mammography-only features yielded an area under the ROC curve (AUC) of 0.74 +/- 0.04, and the DCE-MRI-only features yielded an AUC of 0.78 +/- 0.04. The combination of these two modalities, which included a spiculation feature from mammography and two kinetic features from DCE-MRI, yielded an AUC of 0.87 +/- 0.03. The improvement of combining multimodality information was statistically significant as compared to the use of single modality information alone. CONCLUSIONS A CAD scheme that combines features extracted from FFDM and DCE-MRI images may be advantageous to single-modality CAD in the task of differentiating between malignant and benign lesions.
Collapse
Affiliation(s)
- Yading Yuan
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA.
| | | | | | | | | |
Collapse
|
45
|
A cost-sensitive extension of AdaBoost with markov random field priors for automated segmentation of breast tumors in ultrasonic images. Int J Comput Assist Radiol Surg 2010; 5:537-47. [DOI: 10.1007/s11548-010-0411-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2009] [Accepted: 03/10/2010] [Indexed: 10/19/2022]
|
46
|
Cheng JZ, Chou YH, Huang CS, Chang YC, Tiu CM, Chen KW, Chen CM. Computer-aided US Diagnosis of Breast Lesions by Using Cell-based Contour Grouping. Radiology 2010; 255:746-54. [DOI: 10.1148/radiol.09090001] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
47
|
Alvarenga AV, Teixeira CA, Ruano MG, Pereira WCA. Influence of temperature variations on the entropy and correlation of the Grey-Level Co-occurrence Matrix from B-Mode images. ULTRASONICS 2010; 50:290-293. [PMID: 19800646 DOI: 10.1016/j.ultras.2009.09.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2009] [Revised: 08/13/2009] [Accepted: 09/07/2009] [Indexed: 05/28/2023]
Abstract
In this work, the feasibility of texture parameters extracted from B-Mode images were explored in quantifying medium temperature variation. The goal is to understand how parameters obtained from the gray-level content can be used to improve the actual state-of-the-art methods for non-invasive temperature estimation (NITE). B-Mode images were collected from a tissue mimic phantom heated in a water bath. The phantom is a mixture of water, glycerin, agar-agar and graphite powder. This mixture aims to have similar acoustical properties to in vivo muscle. Images from the phantom were collected using an ultrasound system that has a mechanical sector transducer working at 3.5 MHz. Three temperature curves were collected, and variations between 27 and 44 degrees C during 60 min were allowed. Two parameters (correlation and entropy) were determined from Grey-Level Co-occurrence Matrix (GLCM) extracted from image, and then assessed for non-invasive temperature estimation. Entropy values were capable of identifying variations of 2.0 degrees C. Besides, it was possible to quantify variations from normal human body temperature (37 degrees C) to critical values, as 41 degrees C. In contrast, despite correlation parameter values (obtained from GLCM) presented a correlation coefficient of 0.84 with temperature variation, the high dispersion of values limited the temperature assessment.
Collapse
Affiliation(s)
- André V Alvarenga
- Laboratory of Ultrasound/National Institute of Metrology, Standardization and Industrial Quality (Inmetro), Duque de Caxias, Brazil.
| | | | | | | |
Collapse
|
48
|
Alvarenga AV, Teixeira CA, Ruano MG, Pereira WC. Evaluation of the influence of large temperature variations on the grey level content of B-mode images. ACTA ACUST UNITED AC 2010. [DOI: 10.1016/j.phpro.2010.01.054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
|
49
|
Azevedo CMD, Alvarenga AV, Pereira WCDA, Infantosi AFC. Análise computacional da textura de tumores de mama em imagens por ultrassom de pacientes submetidas a cirurgia conservadora. Radiol Bras 2009. [DOI: 10.1590/s0100-39842009000600009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
OBJETIVO: Avaliar as características de textura de lesões de mama em imagens por ultrassom de pacientes submetidas a cirurgia conservadora que apresentaram, ou não, recidiva. MATERIAIS E MÉTODOS: As imagens de ultrassom de 36 pacientes submetidas a cirurgia conservadora, com 12 tendo apresentado recidiva local e 24 que não apresentaram recidiva no local da cirurgia, foram divididas em: 3 malignas na mama oposta, 7 nódulos benignos, 5 hiperplasias atípicas e 9 alterações fibrocísticas. A textura das lesões foi quantificada utilizando-se dez parâmetros calculados da matriz de coocorrência e da curva de complexidade. Análise discriminante linear foi aplicada aos parâmetros para discriminação de lesões de mama em pacientes submetidas a cirurgia conservadora que apresentaram, ou não, recidiva. RESULTADOS: Avaliando-se a capacidade dos parâmetros em distinguir as recidivas do grupo composto por lesões não recidivas benignas e hiperplasias atípicas, obteve-se especificidade de 100%, com valores de acurácia e sensibilidade superiores a 91%. Num segundo teste, foi possível distinguir as cinco hiperplasias, das lesões não recidivas benignas. CONCLUSÃO: Apesar do número reduzido de casos, os resultados obtidos são encorajadores, sugerindo que o uso da quantificação da textura pode auxiliar na diferenciação entre lesões benignas, hiperplasias atípicas e lesões malignas de origem recidiva.
Collapse
|
50
|
Improvement in diagnosis of breast tumour using ultrasound elastography and echography: A phantom based analysis. Biomed Imaging Interv J 2009; 5:e30. [PMID: 21610995 PMCID: PMC3097724 DOI: 10.2349/biij.5.4.e30] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2009] [Accepted: 09/18/2009] [Indexed: 11/23/2022] Open
Abstract
Due to the isoechoic nature of lesions and their poor contrast with neighbouring tissue, a lesion may remain undetected in ultrasound B mode imaging for cancerous tissue. Imaging of the elastic properties of tissue provides new information which is collateral to tissue pathology. This study provides quantitative analysis of improvements in tumour diagnosis when the ultrasound B mode imaging is combined with elastography. Quantification was based on the textural parameters measured from the ultrasound B mode image and strain measured from the elastogram. The ability of a parameter to discriminate between diseased cases and normal cases was evaluated using receiver operating characteristic (ROC) analysis. Polyacrylamide gel based tissue mimicking phantoms with embedded inclusions of varying stiffness were used for the analysis.
Collapse
|