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Prinzi F, Orlando A, Gaglio S, Vitabile S. Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1038-1053. [PMID: 38351223 PMCID: PMC11169144 DOI: 10.1007/s10278-024-01012-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/20/2023] [Accepted: 12/05/2023] [Indexed: 06/13/2024]
Abstract
Breast microcalcifications are observed in 80% of mammograms, and a notable proportion can lead to invasive tumors. However, diagnosing microcalcifications is a highly complicated and error-prone process due to their diverse sizes, shapes, and subtle variations. In this study, we propose a radiomic signature that effectively differentiates between healthy tissue, benign microcalcifications, and malignant microcalcifications. Radiomic features were extracted from a proprietary dataset, composed of 380 healthy tissue, 136 benign, and 242 malignant microcalcifications ROIs. Subsequently, two distinct signatures were selected to differentiate between healthy tissue and microcalcifications (detection task) and between benign and malignant microcalcifications (classification task). Machine learning models, namely Support Vector Machine, Random Forest, and XGBoost, were employed as classifiers. The shared signature selected for both tasks was then used to train a multi-class model capable of simultaneously classifying healthy, benign, and malignant ROIs. A significant overlap was discovered between the detection and classification signatures. The performance of the models was highly promising, with XGBoost exhibiting an AUC-ROC of 0.830, 0.856, and 0.876 for healthy, benign, and malignant microcalcifications classification, respectively. The intrinsic interpretability of radiomic features, and the use of the Mean Score Decrease method for model introspection, enabled models' clinical validation. In fact, the most important features, namely GLCM Contrast, FO Minimum and FO Entropy, were compared and found important in other studies on breast cancer.
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Affiliation(s)
- Francesco Prinzi
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy.
- Department of Computer Science and Technology, University of Cambridge, CB2 1TN, Cambridge, United Kingdom.
| | - Alessia Orlando
- Section of Radiology - Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University Hospital "Paolo Giaccone", Palermo, Italy
| | - Salvatore Gaglio
- Department of Engineering, University of Palermo, Palermo, Italy
- Institute for High-Performance Computing and Networking, National Research Council (ICAR-CNR), Palermo, Italy
| | - Salvatore Vitabile
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
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Gao Y, Lin J, Zhou Y, Lin R. The application of traditional machine learning and deep learning techniques in mammography: a review. Front Oncol 2023; 13:1213045. [PMID: 37637035 PMCID: PMC10453798 DOI: 10.3389/fonc.2023.1213045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/25/2023] [Indexed: 08/29/2023] Open
Abstract
Breast cancer, the most prevalent malignant tumor among women, poses a significant threat to patients' physical and mental well-being. Recent advances in early screening technology have facilitated the early detection of an increasing number of breast cancers, resulting in a substantial improvement in patients' overall survival rates. The primary techniques used for early breast cancer diagnosis include mammography, breast ultrasound, breast MRI, and pathological examination. However, the clinical interpretation and analysis of the images produced by these technologies often involve significant labor costs and rely heavily on the expertise of clinicians, leading to inherent deviations. Consequently, artificial intelligence(AI) has emerged as a valuable technology in breast cancer diagnosis. Artificial intelligence includes Machine Learning(ML) and Deep Learning(DL). By simulating human behavior to learn from and process data, ML and DL aid in lesion localization reduce misdiagnosis rates, and improve accuracy. This narrative review provides a comprehensive review of the current research status of mammography using traditional ML and DL algorithms. It particularly highlights the latest advancements in DL methods for mammogram image analysis and offers insights into future development directions.
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Affiliation(s)
- Ying’e Gao
- School of Nursing Fujian Medical University, Fuzhou, China
| | - Jingjing Lin
- School of Nursing Fujian Medical University, Fuzhou, China
| | - Yuzhuo Zhou
- Department of Surgery, Hannover Medical School, Hannover, Germany
| | - Rongjin Lin
- School of Nursing Fujian Medical University, Fuzhou, China
- Department of Nursing, the First Affiliated Hospital of Fujian Medical University, Fuzhou, China
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Sarvestani ZM, Jamali J, Taghizadeh M, Dindarloo MHF. A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images. J Cancer Res Clin Oncol 2023:10.1007/s00432-023-04571-y. [PMID: 36680580 DOI: 10.1007/s00432-023-04571-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 01/03/2023] [Indexed: 01/22/2023]
Abstract
PURPOSE Screening programs use mammography as a diagnostic tool for the early detection of breast cancer. Mammogram enhancement is used to increase the local contrast of the mammogram so that the lesions are more visible in the advanced image. For accurate diagnosis in the early stage of breast cancer, the appearance of masses and microcalcification on the mammographic image are two important indicators. The objective of this study was to evaluate the feasibility of the automatic separation of images of breast tissue microcalcifications and also to evaluate its accuracy. METHODS The research was carried out by using two techniques of image enhancement and highlighting of breast tissue microcalcifications for the desired areas by regional ROI based on fuzzy system and also Gabor filtering method. After determining the clusters of breast tissue microcalcifications, the clusters are classified using the decision tree classification algorithm. Then, for segmentation, samples suspected of microcalcification are highlighted and masked, and in the last stage, tissue characteristics are extracted. Subsequently, with the help of an artificial neural network (ANN), determining the benign and malignant types of segmented ROI clusters was accomplished. The proposed system is trained with a Digital Database for Screening Mammography (DDSM) developed by the University of South Florida, USA, and the simulations are performed under MATLAB software and the results are compared with previous work. RESULTS The results of this training performed under this work show an accuracy of 93% and an improvement of sensitivity above 95%. CONCLUSION The result indicates that the proposed approach can be applied to ensure breast cancer diagnosis.
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Affiliation(s)
| | - Jasem Jamali
- Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran.
| | - Mehdi Taghizadeh
- Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
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Zhong J, Huang T, Qiu M, Guan Q, Luo N, Deng Y. A markerless beam's eye view tumor tracking algorithm based on unsupervised deformable registration learning framework of VoxelMorph in medical image with partial data. Phys Med 2023; 105:102510. [PMID: 36535237 DOI: 10.1016/j.ejmp.2022.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 10/18/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
PURPOSE To propose an unsupervised deformable registration learning framework-based markerless beam's eye view (BEV) tumor tracking algorithm for the inferior quality megavolt (MV) images with occlusion and deformation. METHODS Quality assurance (QA) plans for thorax phantom were delivered to the linear accelerator with artificially treatment offsets. Electronic portal imaging device (EPID) images (682 in total) and corresponding digitally reconstructed radiograph (DRR) were gathered as the moving and fixed image pairs, which were randomly divided into training and testing set in a ratio of 0.7:0.3 to train a non-rigid registration model with Voxelmorph. Simultaneously, 533 pairs of patient images from 21 lung tumor plans were acquired for tumor tracking investigation to offer quantifiable tumor motion data. Tracking error and image similarity measures were employed to evaluate the algorithm's accuracy. RESULTS The tracking algorithm can handle image registration with non-rigid deformation and losses ranging from 10 % to 80 %. The tracking errors in the phantom study were below 3 mm in about 86.8 % of cases, and below 2 mm in about 80 % of cases. The normalized mutual information (NMI) changes from 1.182 ± 0.024 to 1.198 ± 0.024 (p < 0.005). The patient target had an average translation of 3.784 mm and a maximum comprehensive displacement value of 7.455 mm. NMI of patient images changes from 1.209 ± 0.027 to 1.217 ± 0.026 (p < 0.005), indicating the presence of non-negligible non-rigid deformation. CONCLUSIONS The study provides a robust markerless tumor tracking algorithm for multi-modal, partial data and inferior quality image processing.
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Affiliation(s)
- Jiajian Zhong
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, PR China
| | - Taiming Huang
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, PR China
| | - Minmin Qiu
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, PR China
| | - Qi Guan
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, PR China
| | - Ning Luo
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, PR China.
| | - Yongjin Deng
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, PR China.
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Fanizzi A, Scognamillo G, Nestola A, Bambace S, Bove S, Comes MC, Cristofaro C, Didonna V, Di Rito A, Errico A, Palermo L, Tamborra P, Troiano M, Parisi S, Villani R, Zito A, Lioce M, Massafra R. Transfer learning approach based on computed tomography images for predicting late xerostomia after radiotherapy in patients with oropharyngeal cancer. Front Med (Lausanne) 2022; 9:993395. [PMID: 36213659 PMCID: PMC9537690 DOI: 10.3389/fmed.2022.993395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 09/01/2022] [Indexed: 11/23/2022] Open
Abstract
Background and purpose Although the latest breakthroughs in radiotherapy (RT) techniques have led to a decrease in adverse event rates, these techniques are still associated with substantial toxicity, including xerostomia. Imaging biomarkers could be useful to predict the toxicity risk related to each individual patient. Our preliminary work aims to develop a radiomic-based support tool exploiting pre-treatment CT images to predict late xerostomia risk in 3 months after RT in patients with oropharyngeal cancer (OPC). Materials and methods We performed a multicenter data collection. We enrolled 61 patients referred to three care centers in Apulia, Italy, out of which 22 patients experienced at least mild xerostomia 3 months after the end of the RT cycle. Pre-treatment CT images, clinical and dose features, and alcohol-smoking habits were collected. We proposed a transfer learning approach to extract quantitative imaging features from CT images by means of a pre-trained convolutional neural network (CNN) architecture. An optimal feature subset was then identified to train an SVM classifier. To evaluate the robustness of the proposed model with respect to different manual contouring practices on CTs, we repeated the same image analysis pipeline on “fake” parotid contours. Results The best performances were achieved by the model exploiting the radiomic features alone. On the independent test, the model reached median AUC, accuracy, sensitivity, and specificity values of 81.17, 83.33, 71.43, and 90.91%, respectively. The model was robust with respect to diverse manual parotid contouring procedures. Conclusion Radiomic analysis could help to develop a valid support tool for clinicians in planning radiotherapy treatment, by providing a risk score of the toxicity development for each individual patient, thus improving the quality of life of the same patient, without compromising patient care.
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Affiliation(s)
| | | | | | - Santa Bambace
- Ospedale Monsignor Raffaele Dimiccoli, Barletta, Italy
| | - Samantha Bove
- IRCCS Istituto Tumori “Giovanni Paolo II,”Bari, Italy
- *Correspondence: Samantha Bove,
| | | | | | | | | | - Angelo Errico
- Ospedale Monsignor Raffaele Dimiccoli, Barletta, Italy
| | | | | | - Michele Troiano
- IRCCS Casa Sollievo della Sofferenza, Opera di San Pio da Pietrelcina Viale Cappuccini, Foggia, Italy
| | - Salvatore Parisi
- IRCCS Casa Sollievo della Sofferenza, Opera di San Pio da Pietrelcina Viale Cappuccini, Foggia, Italy
| | | | - Alfredo Zito
- IRCCS Istituto Tumori “Giovanni Paolo II,”Bari, Italy
| | - Marco Lioce
- IRCCS Istituto Tumori “Giovanni Paolo II,”Bari, Italy
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Kermouni Serradj N, Messadi M, Lazzouni S. Classification of Mammographic ROI for Microcalcification Detection Using Multifractal Approach. J Digit Imaging 2022; 35:1544-1559. [PMID: 35854037 DOI: 10.1007/s10278-022-00677-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 04/12/2022] [Accepted: 06/21/2022] [Indexed: 10/17/2022] Open
Abstract
Microcalcifications (MCs) are the main signs of precancerous cells. The development of aided-system for their detection has become a challenge for researchers in this field. In this paper, we propose a system for MCs detection based on the multifractal approach that classifies mammographic ROIs into normal (healthy) or abnormal ROIs containing MCs. The proposed method is divided into four main steps: a mammogram pre-processing step based on breast selection, breast density reduction using haze removal algorithm and contrast enhancement using multifractal measures. The second step consists of extracting the normal and abnormal ROIs and calculating the multifractal spectrum of each ROI. The next step represents the extraction of the multifractal features from the multifractal spectrum and the GLCM characteristics of each ROI. The last step is the classification of ROIs where three classifiers are tested (KNN, DT, and SVM). The system is evaluated on images from the INbreast database (308 images) with a total of 2688 extracted ROIs (1344 normal, 1344 with MC) from different BI-RADS classes. In this study, the SVM classifier gave the best classification results with a sensitivity, specificity, and precision of 98.66%, 97.77%, and 98.20% respectively. These results are very satisfactory and remarkable compared to the literature.
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Affiliation(s)
- Nadia Kermouni Serradj
- Biomedical Engineering Laboratory, Faculty of Technology, Abou Bekr Belkaid University, 13000, Tlemcen, Algeria.
| | - Mahammed Messadi
- Biomedical Engineering Laboratory, Faculty of Technology, Abou Bekr Belkaid University, 13000, Tlemcen, Algeria
| | - Sihem Lazzouni
- Biomedical Engineering Laboratory, Faculty of Technology, Abou Bekr Belkaid University, 13000, Tlemcen, Algeria
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Basurto-Hurtado JA, Cruz-Albarran IA, Toledano-Ayala M, Ibarra-Manzano MA, Morales-Hernandez LA, Perez-Ramirez CA. Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms. Cancers (Basel) 2022; 14:3442. [PMID: 35884503 PMCID: PMC9322973 DOI: 10.3390/cancers14143442] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/02/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.
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Affiliation(s)
- Jesus A. Basurto-Hurtado
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
| | - Irving A. Cruz-Albarran
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
| | - Manuel Toledano-Ayala
- División de Investigación y Posgrado de la Facultad de Ingeniería (DIPFI), Universidad Autónoma de Querétaro, Cerro de las Campanas S/N Las Campanas, Santiago de Querétaro 76010, Mexico;
| | - Mario Alberto Ibarra-Manzano
- Laboratorio de Procesamiento Digital de Señales, Departamento de Ingeniería Electrónica, Division de Ingenierias Campus Irapuato-Salamanca (DICIS), Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico;
| | - Luis A. Morales-Hernandez
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
| | - Carlos A. Perez-Ramirez
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
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Marathe K, Marasinou C, Li B, Nakhaei N, Li B, Elmore JG, Shapiro L, Hsu W. Automated quantitative assessment of amorphous calcifications: Towards improved malignancy risk stratification. Comput Biol Med 2022; 146:105504. [PMID: 35525068 PMCID: PMC9839357 DOI: 10.1016/j.compbiomed.2022.105504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 03/07/2022] [Accepted: 04/05/2022] [Indexed: 01/17/2023]
Abstract
BACKGROUND Amorphous calcifications noted on mammograms (i.e., small and indistinct calcifications that are difficult to characterize) are associated with high diagnostic uncertainty, often leading to biopsies. Yet, only 20% of biopsied amorphous calcifications are cancer. We present a quantitative approach for distinguishing between benign and actionable (high-risk and malignant) amorphous calcifications using a combination of local textures, global spatial relationships, and interpretable handcrafted expert features. METHOD Our approach was trained and validated on a set of 168 2D full-field digital mammography exams (248 images) from 168 patients. Within these 248 images, we identified 276 image regions with segmented amorphous calcifications and a biopsy-confirmed diagnosis. A set of local (radiomic and region measurements) and global features (distribution and expert-defined) were extracted from each image. Local features were grouped using an unsupervised k-means clustering algorithm. All global features were concatenated with clustered local features and used to train a LightGBM classifier to distinguish benign from actionable cases. RESULTS On the held-out test set of 60 images, our approach achieved a sensitivity of 100%, specificity of 35%, and a positive predictive value of 38% when the decision threshold was set to 0.4. Given that all of the images in our test set resulted in a recommendation of a biopsy, the use of our algorithm would have identified 15 images (25%) that were benign, potentially reducing the number of breast biopsies. CONCLUSIONS Quantitative analysis of full-field digital mammograms can extract subtle shape, texture, and distribution features that may help to distinguish between benign and actionable amorphous calcifications.
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Affiliation(s)
- Kalyani Marathe
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Chrysostomos Marasinou
- Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Beibin Li
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Noor Nakhaei
- Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Bo Li
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Joann G. Elmore
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Linda Shapiro
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA,Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - William Hsu
- Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA,Corresponding author. (W. Hsu)
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Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112412122] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Breast cancer detection using mammogram images at an early stage is an important step in disease diagnostics. We propose a new method for the classification of benign or malignant breast cancer from mammogram images. Hybrid thresholding and the machine learning method are used to derive the region of interest (ROI). The derived ROI is then separated into five different blocks. The wavelet transform is applied to suppress noise from each produced block based on BayesShrink soft thresholding by capturing high and low frequencies within different sub-bands. An improved fractal dimension (FD) approach, called multi-FD (M-FD), is proposed to extract multiple features from each denoised block. The number of features extracted is then reduced by a genetic algorithm. Five classifiers are trained and used with the artificial neural network (ANN) to classify the extracted features from each block. Lastly, the fusion process is performed on the results of five blocks to obtain the final decision. The proposed approach is tested and evaluated on four benchmark mammogram image datasets (MIAS, DDSM, INbreast, and BCDR). We present the results of single- and double-dataset evaluations. Only one dataset is used for training and testing in the single-dataset evaluation, whereas two datasets (one for training, and one for testing) are used in the double-dataset evaluation. The experiment results show that the proposed method yields better results on the INbreast dataset in the single-dataset evaluation, whilst better results are obtained on the remaining datasets in the double-dataset evaluation. The proposed approach outperforms other state-of-the-art models on the Mini-MIAS dataset.
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Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112110372] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The reported incidence of node metastasis at sentinel lymph node biopsy is generally low, so that the majority of women underwent unnecessary invasive axilla surgery. Although the sentinel lymph node biopsy is time consuming and expensive, it is still the intra-operative exam with the highest performance, but sometimes surgery is achieved without a clear diagnosis and also with possible serious complications. In this work, we developed a machine learning model to predict the sentinel lymph nodes positivity in clinically negative patients. Breast cancer clinical and immunohistochemical features of 907 patients characterized by a clinically negative lymph node status were collected. We trained different machine learning algorithms on the retrospective collected data and selected an optimal subset of features through a sequential forward procedure. We found comparable performances for different classification algorithms: on a hold-out training set, the logistics regression classifier with seven features, i.e., tumor diameter, age, histologic type, grading, multiplicity, in situ component and Her2-neu status reached an AUC value of 71.5% and showed a better trade-off between sensitivity and specificity (69.4 and 66.9%, respectively) compared to other two classifiers. On the hold-out test set, the performance dropped by five percentage points in terms of accuracy. Overall, the histological characteristics alone did not allow us to develop a support tool suitable for actual clinical application, but it showed the maximum informative power contained in the same for the resolution of the clinical problem. The proposed study represents a starting point for future development of predictive models to obtain the probability for lymph node metastases by using histopathological features combined with other features of a different nature.
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Loizidou K, Skouroumouni G, Pitris C, Nikolaou C. Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications. Eur Radiol Exp 2021; 5:40. [PMID: 34519867 PMCID: PMC8440760 DOI: 10.1186/s41747-021-00238-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 08/04/2021] [Indexed: 11/24/2022] Open
Abstract
Background Our aim was to demonstrate that automated detection and classification of breast microcalcifications, according to Breast Imaging Reporting and Data System (BI-RADS) categorisation, can be improved with the subtraction of sequential mammograms as opposed to using the most recent image only. Methods One hundred pairs of mammograms were retrospectively collected from two temporally sequential rounds. Fifty percent of the images included no (BI-RADS 1) or benign (BI-RADS 2) microcalcifications. The remaining exhibited suspicious findings (BI-RADS 4-5) in the recent image. Mammograms cannot be directly subtracted, due to tissue changes over time and breast deformation during mammography. To overcome this challenge, optimised preprocessing, image registration, and postprocessing procedures were developed. Machine learning techniques were employed to eliminate false positives (normal tissue misclassified as microcalcifications) and to classify the true microcalcifications as BI-RADS benign or suspicious. Ninety-six features were extracted and nine classifiers were evaluated with and without temporal subtraction. The performance was assessed by measuring sensitivity, specificity, accuracy, and area under the curve (AUC) at receiver operator characteristics analysis. Results Using temporal subtraction, the contrast ratio improved ~ 57 times compared to the most recent mammograms, enhancing the detection of the radiologic changes. Classifying as BI-RADS benign versus suspicious microcalcifications, resulted in 90.3% accuracy and 0.87 AUC, compared to 82.7% and 0.81 using just the most recent mammogram (p = 0.003). Conclusion Compared to using the most recent mammogram alone, temporal subtraction is more effective in the microcalcifications detection and classification and may play a role in automated diagnosis systems. Supplementary Information The online version contains supplementary material available at 10.1186/s41747-021-00238-w.
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Affiliation(s)
- Kosmia Loizidou
- KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, 1 Panepistimiou Avenue, Aglantzia, 2109, Nicosia, Cyprus.
| | - Galateia Skouroumouni
- Nicosia General Hospital, 215 Nicosia-Limassol Old Road, Strovolos, 2029, Nicosia, Cyprus
| | - Costas Pitris
- KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, 1 Panepistimiou Avenue, Aglantzia, 2109, Nicosia, Cyprus
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Massafra R, Bove S, Lorusso V, Biafora A, Comes MC, Didonna V, Diotaiuti S, Fanizzi A, Nardone A, Nolasco A, Ressa CM, Tamborra P, Terenzio A, La Forgia D. Radiomic Feature Reduction Approach to Predict Breast Cancer by Contrast-Enhanced Spectral Mammography Images. Diagnostics (Basel) 2021; 11:diagnostics11040684. [PMID: 33920221 PMCID: PMC8070152 DOI: 10.3390/diagnostics11040684] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/07/2021] [Accepted: 04/08/2021] [Indexed: 02/06/2023] Open
Abstract
Contrast-enhanced spectral mammography (CESM) is an advanced instrument for breast care that is still operator dependent. The aim of this paper is the proposal of an automated system able to discriminate benign and malignant breast lesions based on radiomic analysis. We selected a set of 58 regions of interest (ROIs) extracted from 53 patients referred to Istituto Tumori "Giovanni Paolo II" of Bari (Italy) for the breast cancer screening phase between March 2017 and June 2018. We extracted 464 features of different kinds, such as points and corners of interest, textural and statistical features from both the original ROIs and the ones obtained by a Haar decomposition and a gradient image implementation. The features data had a large dimension that can affect the process and accuracy of cancer classification. Therefore, a classification scheme for dimension reduction was needed. Specifically, a principal component analysis (PCA) dimension reduction technique that includes the calculation of variance proportion for eigenvector selection was used. For the classification method, we trained three different classifiers, that is a random forest, a naïve Bayes and a logistic regression, on each sub-set of principal components (PC) selected by a sequential forward algorithm. Moreover, we focused on the starting features that contributed most to the calculation of the related PCs, which returned the best classification models. The method obtained with the aid of the random forest classifier resulted in the best prediction of benign/malignant ROIs with median values for sensitivity and specificity of 88.37% and 100%, respectively, by using only three PCs. The features that had shown the greatest contribution to the definition of the same were almost all extracted from the LE images. Our system could represent a valid support tool for radiologists for interpreting CESM images.
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Affiliation(s)
- Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (V.D.); (P.T.)
| | - Samantha Bove
- Dipartimento di Matematica, Università degli Studi di Bari, 70121 Bari, Italy;
| | - Vito Lorusso
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (V.L.); (A.N.)
| | - Albino Biafora
- Dipartimento di Economia e Finanza, Università degli Studi di Bari, 70124 Bari, Italy;
| | - Maria Colomba Comes
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (V.D.); (P.T.)
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (V.D.); (P.T.)
| | - Sergio Diotaiuti
- Struttura Semplice Dipartimentale di Chirurgia, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (V.D.); (P.T.)
- Correspondence: ; Tel.: +39-080-555-5111
| | - Annalisa Nardone
- Unita Opertiva Complessa di Radioterapia, IRCCS Istituto Tumori ”Giovanni Paolo II”, 70124 Bari, Italy;
| | - Angelo Nolasco
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (V.L.); (A.N.)
| | - Cosmo Maurizio Ressa
- Unità Operativa Complessa di Chirurgica Plastica e Ricostruttiva, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Pasquale Tamborra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (V.D.); (P.T.)
| | - Antonella Terenzio
- Unità di Oncologia Medica, Università Campus Bio-Medico, 00128 Roma, Italy;
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
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Massafra R, Latorre A, Fanizzi A, Bellotti R, Didonna V, Giotta F, La Forgia D, Nardone A, Pastena M, Ressa CM, Rinaldi L, Russo AOM, Tamborra P, Tangaro S, Zito A, Lorusso V. A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results. Front Oncol 2021; 11:576007. [PMID: 33777733 PMCID: PMC7991309 DOI: 10.3389/fonc.2021.576007] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 01/22/2021] [Indexed: 12/20/2022] Open
Abstract
The mortality associated to breast cancer is in many cases related to metastasization and recurrence. Personalized treatment strategies are critical for the outcomes improvement of BC patients and the Clinical Decision Support Systems can have an important role in medical practice. In this paper, we present the preliminary results of a prediction model of the Breast Cancer Recurrence (BCR) within five and ten years after diagnosis. The main breast cancer-related and treatment-related features of 256 patients referred to Istituto Tumori “Giovanni Paolo II” of Bari (Italy) were used to train machine learning algorithms at the-state-of-the-art. Firstly, we implemented several feature importance techniques and then we evaluated the prediction performances of BCR within 5 and 10 years after the first diagnosis by means different classifiers. By using a small number of features, the models reached highly performing results both with reference to the BCR within 5 years and within 10 years with an accuracy of 77.50% and 80.39% and a sensitivity of 92.31% and 95.83% respectively, in the hold-out sample test. Despite validation studies are needed on larger samples, our results are promising for the development of a reliable prognostic supporting tool for clinicians in the definition of personalized treatment plans.
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Affiliation(s)
- Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Agnese Latorre
- Unitá Opertiva Complessa di Oncologia Medica, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Roberto Bellotti
- Dipartimento di Fisica, Universitá degli Studi "Aldo Moro" e Istituto Nazionale di Fisica Nucleare - Sezione di Bari, Bari, Italy
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Francesco Giotta
- Unitá Opertiva Complessa di Oncologia Medica, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Annalisa Nardone
- Unitá Opertiva Complessa di Radioterapia, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Maria Pastena
- Unitá Opertiva Complessa di Anatomia Patologica, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Cosmo Maurizio Ressa
- Unitá Opertiva Complessa di Chirurgia Plastica e Ricostruttiva, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Lucia Rinaldi
- Struttura Semplice Dipartimentale di Oncologia Per la Presa in Carico Globale del Paziente, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | | | - Pasquale Tamborra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Sabina Tangaro
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universitá degli Studi "Aldo Moro" e Istituto Nazionale di Fisica Nucleare - Sezione di Bari, Bari, Italy
| | - Alfredo Zito
- Unitá Opertiva Complessa di Anatomia Patologica, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Vito Lorusso
- Unitá Opertiva Complessa di Oncologia Medica, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
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14
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Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy. Phys Med 2021; 83:221-241. [DOI: 10.1016/j.ejmp.2021.04.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 03/31/2021] [Accepted: 04/03/2021] [Indexed: 02/06/2023] Open
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15
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A Proposal of Quantum-Inspired Machine Learning for Medical Purposes: An Application Case. MATHEMATICS 2021. [DOI: 10.3390/math9040410] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier adopted to establish a baseline is characterized by the result invariance for the order permutation of the input features, and it exploits stratifications in the training procedure. The quantum one mimics support vector machine mapping in a high-dimensional feature space, yielded by encoding into qubits, while being characterized by complexity. Feature selection is exploited to study the performances associated with a low number of features, thus implemented in a feasible time. Wide variations in sensitivity and specificity are observed in the selected optimal classifiers during cross-validations for both classification system types, with an easier detection of negative or positive cases depending on the choice between the two training schemes. Clinical practice is still far from being reached, even if the flexible structure of quantum-inspired classifier circuits guarantees further developments to rule interactions among features: this preliminary study is solely intended to provide an overview of the particular tree tensor network scheme in a simplified version adopting just product states, as well as to introduce typical machine learning procedures consisting of feature selection and classifier performance evaluation.
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16
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Predicting Molecular Subtypes of Breast Cancer with Mammography and Ultrasound Findings: Introduction of Sono-Mammometry Score. Radiol Res Pract 2021; 2021:6691958. [PMID: 33628504 PMCID: PMC7886512 DOI: 10.1155/2021/6691958] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/19/2021] [Accepted: 01/28/2021] [Indexed: 11/26/2022] Open
Abstract
We studied the correlation of sonographic and digital mammographic features with molecular classification of breast cancer. Imaging features from 313 patients with preliminary ultrasound and digital mammogram between November 2017 and May 2020 were compared with histopathology and immunohistochemical analysis for the prediction of molecular classification of breast cancer. We also devised a score called “sono-mammometry” score consisting of few simple imaging features which can easily be performed in outpatient settings. We studied that non-triple-negative breast cancers are predominantly hypoechoic and strongly correlate with the presence of irregular spiculated margins along with peripheral echogenic halo, posterior shadowing, and microcalcifications, while there is considerable variation in imaging features of TNBC as some of its imaging features overlap with those of typical benign tumors. Although imaging characteristics are helpful in the prediction of molecular classification, the prognostication value of these imaging features is still weak. There is considerable variation in imaging features which warrants vigilance towards improved diagnostic performance. To help better understand these features, our sono-mammometry score can serve as straightforward test which is assumed to be functional and productive in resource-limited settings.
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17
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Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study. Cancers (Basel) 2021; 13:cancers13020352. [PMID: 33477893 PMCID: PMC7833376 DOI: 10.3390/cancers13020352] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 01/14/2021] [Accepted: 01/15/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary Sentinel lymph node biopsy procedure is time consuming and expensive, but it is still the intra-operative exam capable of the best performance. However, sometimes, surgery is achieved without a clear diagnosis, so clinical decision support systems developed with artificial intelligence techniques are essential to assist current diagnostic procedures. In this work, we evaluated the usefulness of a CancerMath tool in the sentinel lymph nodes positivity prediction for clinically negative patients. We tested it on 993 patients referred to our institute characterized by sentinel lymph node status, tumor size, age, histologic type, grading, expression of estrogen receptor, progesterone receptor, HER2, and Ki-67. By training the CancerMath (CM) model on our dataset, we reached a sensitivity value of 72%, whereas the online one was 46%, despite a specificity reduction. It was found the addiction of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients. Abstract In the absence of lymph node abnormalities detectable on clinical examination or imaging, the guidelines provide for the dissection of the first axillary draining lymph nodes during surgery. It is not always possible to arrive at surgery without diagnostic doubts, and machine learning algorithms can support clinical decisions. The web calculator CancerMath (CM) allows you to estimate the probability of having positive lymph nodes valued on the basis of tumor size, age, histologic type, grading, expression of estrogen receptor, and progesterone receptor. We collected 993 patients referred to our institute with clinically negative results characterized by sentinel lymph node status, prognostic factors defined by CM, and also human epidermal growth factor receptor 2 (HER2) and Ki-67. Area Under the Curve (AUC) values obtained by the online CM application were comparable with those obtained after training its algorithm on our database. Nevertheless, by training the CM model on our dataset and using the same feature, we reached a sensitivity median value of 72%, whereas the online one was equal to 46%, despite a specificity reduction. We found that the addition of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients with the aim of reducing as much as possible the false positives that lead to axillary dissection. As showed by our experimental results, it is not particularly suitable for use as a support instrument for the prediction of metastatic lymph nodes on clinically negative patients.
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18
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Shen L, Ma X, Jiang T, Shen X, Yang W, You C, Peng W. Malignancy Risk Stratification Prediction of Amorphous Calcifications Based on Clinical and Mammographic Features. Cancer Manag Res 2021; 13:235-245. [PMID: 33469367 PMCID: PMC7811441 DOI: 10.2147/cmar.s286269] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 12/17/2020] [Indexed: 12/16/2022] Open
Abstract
Purpose To explore the potential factors influencing the malignancy risk of amorphous calcifications and establish a predictive nomogram for malignancy risk stratification. Patients and Methods Consecutive mammograms from January 2013 to December 2018 were retrospectively reviewed. Traditional clinical features were recorded, and mammographic features were estimated according to the 5th BI-RADS. Included calcifications were randomly divided into the training and validation cohorts. A nomogram was developed to graphically predict the risk of malignancy (risk) based on stepwise multivariate logistic regression analysis. The discrimination and calibration performance of the model were assessed in both the training and validation cohorts. Results Finally, 1018 amorphous calcifications with final pathological results in 907 women were identified with a malignancy rate of 28.4% (95% CI: 25.7%, 31.3%). The malignancy rates of subgroups divided by the distribution of calcifications, quantity of calcifications, age, menopausal status and family history of cancer were significantly different. There were 712 cases and 306 cases in the training and validation cohorts. The prediction nomogram was finally developed based on four risk factors, including age and distribution, maximum diameter and quantity of calcifications. The AUC of the nomogram was 0.799 (95% CI: 0.761, 0.836) in the training cohort and 0.795 (95% CI: 0.738, 0.852) in the validation cohort. Conclusion On mammography, the distribution, maximum diameter and quantity of calcifications are independent predictors of malignant amorphous calcifications and can be easily obtained in the clinic. The nomogram developed in this study for individualized malignancy risk stratification of amorphous calcifications shows good discrimination performance.
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Affiliation(s)
- Lijuan Shen
- Shanghai Institute of Medical Imaging, Shanghai, People's Republic of China.,Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Xiaowen Ma
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Tingting Jiang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Xigang Shen
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Wentao Yang
- Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
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19
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Hu H, Zhang M, Liu Y, Li XR, Liu G, Wang Z. Mammary hamartoma: is ultrasound-guided vacuum-assisted breast biopsy sufficient for its treatment? Gland Surg 2020; 9:1278-1285. [PMID: 33224802 DOI: 10.21037/gs-20-437] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Mammary hamartomas were mostly benign tumors with rare rate of recurrence and malignant transformation. Ultrasound (US)-guided vacuum-assisted breast biopsy (VABB) has been reported sufficiently safe in treating many breast benign tumors but remained undefined in mammary hamartoma for its usual underdiagnosis in US. Thus, this study aims to evaluate the efficiency of US-guided VABB in treating mammary hamartomas. Methods From May 2015 to March 2019, 3,388 lesions of 2,534 patients underwent percutaneous US-guided VABB, among which 31 mammary hamartomas proved by pathology were included in this study. Patients were followed up by US three, six and twelve months later, then at 1-year intervals. Lesions were classified to analyze the possible factors associated with excision rate, bleeding volume and complications. Results Of the 31 patients, recurrence was seen in 1 case in 1 year after the procedure and complete excision rate was 96.8% (30/31). The bleeding volume ranged from 1 to 15 mL (mean number ± standard deviation, 6.5±3.4 mL) and significant statistical differences were detected in patient age and the largest diameter of lesions. The main complications included pain (22.6%), hematomas (9.7%) and ecchymosis (3.2%). Conclusions US-guided VABB ensures an outstanding complete excision rate and provides an alternative solution to treat mammary hamartomas.
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Affiliation(s)
- Huayu Hu
- School of Medicine, Nankai University, Tianjin, China.,Department of General Surgery, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Mengke Zhang
- Department of Ultrasound, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Yuan Liu
- Department of Ultrasound, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Xi-Ru Li
- Department of General Surgery, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Gang Liu
- Department of Radiology, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Zhili Wang
- Department of Ultrasound, Chinese People's Liberation Army General Hospital, Beijing, China
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20
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Gao G, Shi X, Yao Z, Shen J, Shen L. Identification of lymph node metastasis-related microRNAs in breast cancer using bioinformatics analysis. Medicine (Baltimore) 2020; 99:e22105. [PMID: 32991406 PMCID: PMC7523764 DOI: 10.1097/md.0000000000022105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Lymph node metastasis is a significant problem in breast cancer, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism and to explore the key RNAs and pathways that mediate lymph node metastasis in breast cancer. METHODS GSE100453 and GSE38167 were downloaded from the Gene Expression Omnibus (GEO) database and 569 breast cancer statistics were also downloaded from the TCGA database. Differentially expressed miRNAs were calculated by using R software and GEO2R. Gene ontology and Enriched pathway analysis of target mRNAs were analyzed by using the Database for Database of Annotation Visualization and Integrated Discovery (DAVID) and R software. The protein-protein interaction (PPI) network was performed according to Metascape, String, and Cytoscape software. RESULTS In total, 6 differentially expressed miRNAs were selected, and 499 mRNAs were identified after filtering. The research of the Kyoto Encyclopedia of Genes and Genomes (KEGG) demonstrated that mRNAs enriched in certain tumor pathways. Also, certain hub mRNAs were highlighted after constructed and analyzed the PPI network. A total of 3 out of 6 miRNAs had a significant relationship with the overall survival (P < .05) and showed a good ability of risk prediction model of over survival. CONCLUSIONS By utilizing bioinformatics analyses, differently expressed miRNAs were identified and constructed a complete gene network. Several potential mechanisms and therapeutic and prognostic targets of lymph node metastasis were also demonstrated in breast cancer.
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La Forgia D, Fanizzi A, Campobasso F, Bellotti R, Didonna V, Lorusso V, Moschetta M, Massafra R, Tamborra P, Tangaro S, Telegrafo M, Pastena MI, Zito A. Radiomic Analysis in Contrast-Enhanced Spectral Mammography for Predicting Breast Cancer Histological Outcome. Diagnostics (Basel) 2020; 10:E708. [PMID: 32957690 PMCID: PMC7555402 DOI: 10.3390/diagnostics10090708] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 09/07/2020] [Accepted: 09/16/2020] [Indexed: 02/07/2023] Open
Abstract
Contrast-Enhanced Spectral Mammography (CESM) is a recently introduced mammographic method with characteristics particularly suitable for breast cancer radiomic analysis. This work aims to evaluate radiomic features for predicting histological outcome and two cancer molecular subtypes, namely Human Epidermal growth factor Receptor 2 (HER2)-positive and triple-negative. From 52 patients, 68 lesions were identified and confirmed on histological examination. Radiomic analysis was performed on regions of interest (ROIs) selected from both low-energy (LE) and ReCombined (RC) CESM images. Fourteen statistical features were extracted from each ROI. Expression of estrogen receptor (ER) was significantly correlated with variation coefficient and variation range calculated on both LE and RC images; progesterone receptor (PR) with skewness index calculated on LE images; and Ki67 with variation coefficient, variation range, entropy and relative smoothness indices calculated on RC images. HER2 was significantly associated with relative smoothness calculated on LE images, and grading tumor with variation coefficient, entropy and relative smoothness calculated on RC images. Encouraging results for differentiation between ER+/ER-, PR+/PR-, HER2+/HER2-, Ki67+/Ki67-, High-Grade/Low-Grade and TN/NTN were obtained. Specifically, the highest performances were obtained for discriminating HER2+/HER2- (90.87%), ER+/ER- (83.79%) and Ki67+/Ki67- (84.80%). Our results suggest an interesting role for radiomics in CESM to predict histological outcomes and particular tumors' molecular subtype.
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Affiliation(s)
- Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.F.); (V.D.); (P.T.)
| | - Francesco Campobasso
- Dipartimento di Economia e Finanza, Università degli Studi di Bari “Aldo Moro”, Largo Abbazia S. Scolastica, 70124 Bari, Italy;
| | - Roberto Bellotti
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari “Aldo Moro”, Via Giovanni Amendola, 165/a, 70126 Bari, Italy;
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via Giovanni Amendola, 165/a, 70126 Bari, Italy;
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.F.); (V.D.); (P.T.)
| | - Vito Lorusso
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Marco Moschetta
- Unità Operativa Semplice Dipartimentale Radiodiagnostica ad Indirizzo Senologico, Azienda Ospedaliero-Universitaria Consorziale Policlinico, Piazza Giulio Cesare 11, 70124 Bari, Italy; (M.M.); (M.T.)
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.F.); (V.D.); (P.T.)
| | - Pasquale Tamborra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.F.); (V.D.); (P.T.)
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via Giovanni Amendola, 165/a, 70126 Bari, Italy;
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70121 Bari, Italy
| | - Michele Telegrafo
- Unità Operativa Semplice Dipartimentale Radiodiagnostica ad Indirizzo Senologico, Azienda Ospedaliero-Universitaria Consorziale Policlinico, Piazza Giulio Cesare 11, 70124 Bari, Italy; (M.M.); (M.T.)
| | - Maria Irene Pastena
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.I.P.); (A.Z.)
| | - Alfredo Zito
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.I.P.); (A.Z.)
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Fausto A, Fanizzi A, Volterrani L, Mazzei FG, Calabrese C, Casella D, Marcasciano M, Massafra R, La Forgia D, Mazzei MA. Feasibility, Image Quality and Clinical Evaluation of Contrast-Enhanced Breast MRI Performed in a Supine Position Compared to the Standard Prone Position. Cancers (Basel) 2020; 12:cancers12092364. [PMID: 32825583 PMCID: PMC7564182 DOI: 10.3390/cancers12092364] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 08/12/2020] [Accepted: 08/19/2020] [Indexed: 11/16/2022] Open
Abstract
Background: To assess the feasibility, image quality and diagnostic value of contrast-enhanced breast magnetic resonance imaging (MRI) performed in a supine compared to a prone position. Methods: One hundred and fifty-one patients who had undergone a breast MRI in both the standard prone and supine position were evaluated retrospectively. Two 1.5 T MR scanners were used with the same image resolution, sequences and contrast medium in all examinations. The image quality and the number and dimensions of lesions were assessed by two expert radiologists in an independent and randomized fashion. Two different classification systems were used. Histopathology was the standard of reference. Results: Two hundred and forty MRIs from 120 patients were compared. The analysis revealed 134 MRIs with monofocal (U), 68 with multifocal (M) and 38 with multicentric (C) lesions. There was no difference between the image quality and number of lesions in the prone and supine examinations. A significant difference in the lesion extension was observed between the prone and supine position. No significant differences emerged in the classification of the lesions detected in the prone compared to the supine position. Conclusions: It is possible to perform breast MRI in a supine position with the same image quality, resolution and diagnostic value as in a prone position. In the prone position, the lesion dimensions are overestimated with a higher wash-in peak than in the supine position.
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Affiliation(s)
- Alfonso Fausto
- Department of Diagnostic Imaging, University Hospital of Siena, Azienda Ospedaliera Universitaria Senese, 53100 Siena, Italy;
- Correspondence: ; Tel.: +39-0577585287 or +39-3477601341
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy; (A.F.); (R.M.)
| | - Luca Volterrani
- Department of Medical, Surgical and Neuro Sciences, Unit of Diagnostic Imaging, University Hospital of Siena, Azienda Ospedaliera Universitaria Senese, 53100 Siena, Italy; (L.V.); (M.A.M.)
| | - Francesco Giuseppe Mazzei
- Department of Diagnostic Imaging, University Hospital of Siena, Azienda Ospedaliera Universitaria Senese, 53100 Siena, Italy;
| | | | - Donato Casella
- Department of Oncologic and Reconstructive Breast Surgery, Azienda Ospedaliera Universitaria Senese, University Hospital of Siena, 53100 Siena, Italy;
| | - Marco Marcasciano
- Unità di Oncologia Chirurgica Ricostruttiva della Mammella, “Spedali Riuniti” di Livorno, Breast Unit Integrata di Livorno Cecina, Piombino Elba, Azienda USL Toscana Nord Ovest, 57100 Livorno, Italy;
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy; (A.F.); (R.M.)
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy;
| | - Maria Antonietta Mazzei
- Department of Medical, Surgical and Neuro Sciences, Unit of Diagnostic Imaging, University Hospital of Siena, Azienda Ospedaliera Universitaria Senese, 53100 Siena, Italy; (L.V.); (M.A.M.)
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Chang RWJ, Chuang SL, Hsu CY, Yen AMF, Wu WYY, Chen SLS, Fann JCY, Tabar L, Smith RA, Duffy SW, Chiu SYH, Chen HH. Precision Science on Incidence and Progression of Early-Detected Small Breast Invasive Cancers by Mammographic Features. Cancers (Basel) 2020; 12:E1855. [PMID: 32664200 PMCID: PMC7408735 DOI: 10.3390/cancers12071855] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 06/25/2020] [Accepted: 07/06/2020] [Indexed: 11/21/2022] Open
Abstract
The aim was to evaluate how the inter-screening interval affected the performance of screening by mammographic appearances. This was a Swedish retrospective screening cohort study with information on screening history and mammography features in two periods (1977-1985 and 1996-2010). The pre-clinical incidence and the mean sojourn time (MST) for small breast cancer allowing for sensitivity by mammographic appearances were estimated. The percentage of interval cancer against background incidence (I/E ratio) was used to assess the performance of mammography screening by different inter-screening intervals. The sensitivity-adjusted MSTs (in years) were heterogeneous with mammographic features, being longer for powdery and crushed stone-like calcifications (4.26, (95% CI, 3.50-5.26)) and stellate masses (3.76, (95% CI, 3.15-4.53)) but shorter for circular masses (2.65, (95% CI, 2.06-3.55)) in 1996-2010. The similar trends, albeit longer MSTs, were also noted in 1977-1985. The I/E ratios for the stellate type were 23% and 32% for biennial and triennial screening, respectively. The corresponding figures were 32% and 43% for the circular type and 21% and 29% for powdery and crushed stone-like calcifications, respectively. Mammography-featured progressions of small invasive breast cancer provides a new insight into personalized quality assurance, surveillance, treatment and therapy of early-detected breast cancer.
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Affiliation(s)
- Rene Wei-Jung Chang
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei City 100, Taiwan; (R.W.-J.C.); (C.-Y.H.)
| | - Shu-Lin Chuang
- Department of Medical Research, National Taiwan University Hospital, Taipei City 100, Taiwan;
| | - Chen-Yang Hsu
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei City 100, Taiwan; (R.W.-J.C.); (C.-Y.H.)
| | - Amy Ming-Fang Yen
- School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei City 110, Taiwan; (A.M.-F.Y.); (S.L.-S.C.)
| | - Wendy Yi-Ying Wu
- Department of Radiation Sciences, Oncology, Umeå University, 90187 Umeå, Sweden;
| | - Sam Li-Sheng Chen
- School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei City 110, Taiwan; (A.M.-F.Y.); (S.L.-S.C.)
| | - Jean Ching-Yuan Fann
- Department of Health Industry Management, College of Healthcare Management, Kainan University, Taoyuan City 338, Taiwan;
| | - Laszlo Tabar
- Department of Mammography, Falun Central Hospital, 791823 Falun, Sweden;
| | - Robert A. Smith
- Center for Cancer Screening, American Cancer Society, Atlanta, GA 30303, USA;
| | - Stephen W. Duffy
- Centre for Cancer Prevention, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK;
| | - Sherry Yueh-Hsia Chiu
- Department of Health Care Management, College of Management, Chang Gung University, Taoyuan City 333, Taiwan
- Division of Hepatogastroenterology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City 833, Taiwan
| | - Hsiu-Hsi Chen
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei City 100, Taiwan; (R.W.-J.C.); (C.-Y.H.)
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Njor SH, Vejborg I, Larsen MB. Breast cancer survivors' risk of interval cancers and false positive results in organized mammography screening. Cancer Med 2020; 9:6042-6050. [PMID: 32608178 PMCID: PMC7433834 DOI: 10.1002/cam4.3182] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 05/05/2020] [Accepted: 05/07/2020] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Breast cancer survivors are increasing followed for new breast cancers / recurrences by mammography screening only. We aimed at assessing how often breast cancer survivors get a false positive or false negative result at mammography screening. METHODS All mammography screenings performed between 2007 and 2017 in the Danish national mammography screening programme were included. Screenings in women with a breast cancer diagnosis prior to invitation were included in the "breast cancer survivors" group, while remaining screenings were included in the "no previous breast cancer" group. We compared the proportion of false positive screenings and the proportion of breast cancers detected at screening among breast cancer survivors and women without previous breast cancer. The analyses were further stratified according to whether the women had a diagnostic breast imaging in the 21 months prior to the screening. RESULTS At initial screenings, breast cancer survivors had a significant lower false positive risk than other women, while the risk was similar at subsequent screenings. Breast cancer survivors had a significant lower proportion of breast cancers detected at screening compared to other women. This was true both for women who had a diagnostic breast imaging in the 21 months prior to screening and those who did not. CONCLUSION This study shows that breast cancers survivors have a smaller amount of their new breast cancers detected at mammography screening, when compare to the amount of new breast cancers detected at mammography screening among women without previous breast cancer. The lower sensitivity does not seem to be due to different behavior among breast cancer survivors.
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Affiliation(s)
- Sisse Helle Njor
- Department of Public Health Programmes, Regional Hospital Randers, Randers, Denmark.,Department of Clinical Medicine, Aarhus University, aarhus, Denmark
| | - Ilse Vejborg
- Department of Radiology, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - Mette Bach Larsen
- Department of Public Health Programmes, Regional Hospital Randers, Randers, Denmark
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Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images. Diagnostics (Basel) 2020; 10:diagnostics10050330. [PMID: 32443922 PMCID: PMC7277981 DOI: 10.3390/diagnostics10050330] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 05/15/2020] [Accepted: 05/18/2020] [Indexed: 12/24/2022] Open
Abstract
We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.
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La Forgia D, Fausto A, Gatta G, Di Grezia G, Faggian A, Fanizzi A, Cutrignelli D, Dentamaro R, Didonna V, Lorusso V, Massafra R, Tangaro S, Mazzei MA. Elite VABB 13G: A New Ultrasound-Guided Wireless Biopsy System for Breast Lesions. Technical Characteristics and Comparison with Respect to Traditional Core-Biopsy 14-16G Systems. Diagnostics (Basel) 2020; 10:diagnostics10050291. [PMID: 32397505 PMCID: PMC7277965 DOI: 10.3390/diagnostics10050291] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 05/01/2020] [Accepted: 05/05/2020] [Indexed: 02/07/2023] Open
Abstract
The typification of breast lumps with fine-needle biopsies is often affected by inconclusive results that extend diagnostic time. Many breast centers have progressively substituted cytology with micro-histology. The aim of this study is to assess the performance of a 13G-needle biopsy using cable-free vacuum-assisted breast biopsy (VABB) technology. Two of our operators carried out 200 micro-histological biopsies using the Elite 13G-needle VABB and 1314 14–16G-needle core biopsies (CBs) on BI-RADS 3, 4, and 5 lesions. Thirty-one of the procedures were repeated following CB, eighteen following cytological biopsy, and three after undergoing both procedures. The VABB Elite procedure showed high diagnostic performance with an accuracy of 94.00%, a sensitivity of 92.30%, and a specificity of 100%, while the diagnostic underestimation was 11.00%, all significantly comparable to of the CB procedure. The VABB Elite 13G system has been shown to be a simple, rapid, reliable, and well-tolerated biopsy procedure, without any significant complications and with a diagnostic performance comparable to traditional CB procedures. The histological class change in an extremely high number of samples would suggest the use of this procedure as a second-line biopsy for suspect cases or those with indeterminate cyto-histological results.
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Affiliation(s)
- Daniele La Forgia
- Radiodiagnostica Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (D.L.F.); (R.D.)
| | - Alfonso Fausto
- Dipartimento di Diagnostica per Immagini, Azienda Ospedaliera Universitaria Senese, Viale Bracci 10, 53100 Siena, Italy; (A.F.); (M.A.M.)
| | - Gianluca Gatta
- Dipartimento Medicina di Precisione, Università degli Studi della Campania Luigi Vanvitelli, Piazza L. Miraglia 2, 80138 Napoli, Italy;
| | - Graziella Di Grezia
- Dipartimento dei Servizi—Diagnostica per Immagini, Ospedale “G. Criscuoli”, Via Quadrivio, 83054 Avellino, Italy;
| | - Angela Faggian
- UOC Diagnostica per Immagini, Azienda Ospedaliera San Pio, Via dell’Angelo 1, 82100 Benevento, Italy;
| | - Annarita Fanizzi
- Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
- Correspondence: ; Tel.: +39-080-5555111
| | - Daniela Cutrignelli
- Chirurgia Plastica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Rosalba Dentamaro
- Radiodiagnostica Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (D.L.F.); (R.D.)
| | - Vittorio Didonna
- Fisica Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (V.D.); (R.M.)
| | - Vito Lorusso
- Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Raffaella Massafra
- Fisica Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (V.D.); (R.M.)
| | - Sabina Tangaro
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari ‘Aldo Moro’, 70125 Bari, Italy;
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via Giovanni Amendola, 165/a, 70126 Bari, Italy
| | - Maria Antonietta Mazzei
- Dipartimento di Diagnostica per Immagini, Azienda Ospedaliera Universitaria Senese, Viale Bracci 10, 53100 Siena, Italy; (A.F.); (M.A.M.)
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Valenzuela O, Rojas F, Rojas I, Glosekotter P. Main findings and advances in bioinformatics and biomedical engineering- IWBBIO 2018. BMC Bioinformatics 2020; 21:153. [PMID: 32366219 PMCID: PMC7199304 DOI: 10.1186/s12859-020-3467-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In the current supplement, we are proud to present seventeen relevant contributions from the 6th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2018), which was held during April 25-27, 2018 in Granada (Spain). These contributions have been chosen because of their quality and the importance of their findings.
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Affiliation(s)
- Olga Valenzuela
- Faculty of Sciences, Applied Mathematics, University of Granada, Avenida de Fuente Nueva, Granada, 18071 Spain
| | - Fernando Rojas
- Information and Communications Technology Centre (CITIC and ETSIIT-UGR) University of Granada, Periodista Daniel Saucedo Aranda, Granada, 18071 Spain
| | - Ignacio Rojas
- Information and Communications Technology Centre (CITIC and ETSIIT-UGR) University of Granada, Periodista Daniel Saucedo Aranda, Granada, 18071 Spain
| | - Peter Glosekotter
- Department of Electrical Engineering and Computer Science, University of Applied Sciences of Munster, Stegerweldstr 39, Steinfurt, 48565 Germany
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