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Bayareh-Mancilla R, Medina-Ramos LA, Toriz-Vázquez A, Hernández-Rodríguez YM, Cigarroa-Mayorga OE. Automated Computer-Assisted Medical Decision-Making System Based on Morphological Shape and Skin Thickness Analysis for Asymmetry Detection in Mammographic Images. Diagnostics (Basel) 2023; 13:3440. [PMID: 37998576 PMCID: PMC10670641 DOI: 10.3390/diagnostics13223440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 07/28/2023] [Accepted: 08/03/2023] [Indexed: 11/25/2023] Open
Abstract
Breast cancer is a significant health concern for women, emphasizing the need for early detection. This research focuses on developing a computer system for asymmetry detection in mammographic images, employing two critical approaches: Dynamic Time Warping (DTW) for shape analysis and the Growing Seed Region (GSR) method for breast skin segmentation. The methodology involves processing mammograms in DICOM format. In the morphological study, a centroid-based mask is computed using extracted images from DICOM files. Distances between the centroid and the breast perimeter are then calculated to assess similarity through Dynamic Time Warping analysis. For skin thickness asymmetry identification, a seed is initially set on skin pixels and expanded based on intensity and depth similarities. The DTW analysis achieves an accuracy of 83%, correctly identifying 23 possible asymmetry cases out of 20 ground truth cases. The GRS method is validated using Average Symmetric Surface Distance and Relative Volumetric metrics, yielding similarities of 90.47% and 66.66%, respectively, for asymmetry cases compared to 182 ground truth segmented images, successfully identifying 35 patients with potential skin asymmetry. Additionally, a Graphical User Interface is designed to facilitate the insertion of DICOM files and provide visual representations of asymmetrical findings for validation and accessibility by physicians.
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Affiliation(s)
- Rafael Bayareh-Mancilla
- Department Advanced Technologies, UPIITA-Instituto Politécnico Nacional, Av. IPN No. 2580, Mexico City C.P. 07340, Mexico
| | | | - Alfonso Toriz-Vázquez
- Academic Unit, Institute of Applied Mathematics and Systems Research of the State of Yucatan, National Autonomous University of Mexico, Merida C.P. 97302, Yucatan, Mexico
| | | | - Oscar Eduardo Cigarroa-Mayorga
- Department Advanced Technologies, UPIITA-Instituto Politécnico Nacional, Av. IPN No. 2580, Mexico City C.P. 07340, Mexico
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Shimokawa D, Takahashi K, Oba K, Takaya E, Usuzaki T, Kadowaki M, Kawaguchi K, Adachi M, Kaneno T, Fukuda T, Yagishita K, Tsunoda H, Ueda T. Deep learning model for predicting the presence of stromal invasion of breast cancer on digital breast tomosynthesis. Radiol Phys Technol 2023; 16:406-413. [PMID: 37466807 DOI: 10.1007/s12194-023-00731-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 07/03/2023] [Accepted: 07/03/2023] [Indexed: 07/20/2023]
Abstract
To develop a deep learning (DL)-based algorithm to predict the presence of stromal invasion in breast cancer using digital breast tomosynthesis (DBT). Our institutional review board approved this retrospective study and waived the requirement for informed consent from the patients. Initially, 499 patients (mean age 50.5 years, age range, 29-90 years) who were referred to our hospital under the suspicion of breast cancer and who underwent DBT between March 1 and August 31, 2019, were enrolled in this study. Among the 499 patients, 140 who underwent surgery after being diagnosed with breast cancer were selected for the analysis. Based on the pathological reports, the 140 patients were classified into two groups: those with non-invasive cancer (n = 20) and those with invasive cancer (n = 120). VGG16, Resnet50, DenseNet121, and Xception architectures were used as DL models to differentiate non-invasive from invasive cancer. The diagnostic performance of the DL models was assessed based on the area under the receiver operating characteristic curve (AUC). The AUC for the four models were 0.56 [95% confidence intervals (95% CI) 0.49-0.62], 0.67 (95% CI 0.62-0.74), 0.71 (95% CI 0.65-0.75), and 0.75 (95% CI 0.69-0.81), respectively. Our proposed DL model trained on DBT images is useful for predicting the presence of stromal invasion in breast cancer.
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Affiliation(s)
- Daiki Shimokawa
- Department of Clinical Imaging, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8575, Japan
| | - Kengo Takahashi
- Department of Clinical Imaging, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8575, Japan
| | - Ken Oba
- Department of Radiology, St. Luke's International Hospital, 9-1, Akashi-Cho, Chuo-Ku, Tokyo, 104-8560, Japan
| | - Eichi Takaya
- Department of Clinical Imaging, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8575, Japan
- AI Lab, Tohoku University Hospital, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8574, Japan
| | - Takuma Usuzaki
- Department of Clinical Imaging, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8575, Japan
| | - Mizuki Kadowaki
- Department of Clinical Imaging, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8575, Japan
| | - Kurara Kawaguchi
- Department of Clinical Imaging, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8575, Japan
| | - Maki Adachi
- Department of Clinical Imaging, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8575, Japan
| | - Tomofumi Kaneno
- Department of Clinical Imaging, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8575, Japan
| | - Toshinori Fukuda
- Department of Radiology, St. Luke's International Hospital, 9-1, Akashi-Cho, Chuo-Ku, Tokyo, 104-8560, Japan
| | - Kazuyo Yagishita
- Department of Radiology, St. Luke's International Hospital, 9-1, Akashi-Cho, Chuo-Ku, Tokyo, 104-8560, Japan
| | - Hiroko Tsunoda
- Department of Radiology, St. Luke's International Hospital, 9-1, Akashi-Cho, Chuo-Ku, Tokyo, 104-8560, Japan
| | - Takuya Ueda
- Department of Clinical Imaging, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8575, Japan.
- AI Lab, Tohoku University Hospital, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8574, Japan.
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Retson TA, Eghtedari M. Expanding Horizons: The Realities of CAD, the Promise of Artificial Intelligence, and Machine Learning's Role in Breast Imaging beyond Screening Mammography. Diagnostics (Basel) 2023; 13:2133. [PMID: 37443526 DOI: 10.3390/diagnostics13132133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/06/2023] [Accepted: 06/12/2023] [Indexed: 07/15/2023] Open
Abstract
Artificial intelligence (AI) applications in mammography have gained significant popular attention; however, AI has the potential to revolutionize other aspects of breast imaging beyond simple lesion detection. AI has the potential to enhance risk assessment by combining conventional factors with imaging and improve lesion detection through a comparison with prior studies and considerations of symmetry. It also holds promise in ultrasound analysis and automated whole breast ultrasound, areas marked by unique challenges. AI's potential utility also extends to administrative tasks such as MQSA compliance, scheduling, and protocoling, which can reduce the radiologists' workload. However, adoption in breast imaging faces limitations in terms of data quality and standardization, generalizability, benchmarking performance, and integration into clinical workflows. Developing methods for radiologists to interpret AI decisions, and understanding patient perspectives to build trust in AI results, will be key future endeavors, with the ultimate aim of fostering more efficient radiology practices and better patient care.
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Affiliation(s)
- Tara A Retson
- Department of Radiology, University of California, San Diego, CA 92093, USA
| | - Mohammad Eghtedari
- Department of Radiology, University of California, San Diego, CA 92093, USA
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Ali R, Balamurali M, Varamini P. Deep Learning-Based Artificial Intelligence to Investigate Targeted Nanoparticles' Uptake in TNBC Cells. Int J Mol Sci 2022; 23:ijms232416070. [PMID: 36555718 PMCID: PMC9785476 DOI: 10.3390/ijms232416070] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/08/2022] [Accepted: 12/11/2022] [Indexed: 12/23/2022] Open
Abstract
Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer in women. It has the poorest prognosis along with limited therapeutic options. Smart nano-based carriers are emerging as promising approaches in treating TNBC due to their favourable characteristics such as specifically delivering different cargos to cancer cells. However, nanoparticles' tumour cell uptake, and subsequent drug release, are essential factors considered during the drug development process. Contemporary qualitative analyses based on imaging are cumbersome and prone to human biases. Deep learning-based algorithms have been well-established in various healthcare settings with promising scope in drug discovery and development. In this study, the performance of five different convolutional neural network models was evaluated. In this research, we investigated two sequential models from scratch and three pre-trained models, VGG16, ResNet50, and Inception V3. These models were trained using confocal images of nanoparticle-treated cells loaded with a fluorescent anticancer agent. Comparative and cross-validation analyses were further conducted across all models to obtain more meaningful results. Our models showed high accuracy in predicting either high or low drug uptake and release into TNBC cells, indicating great translational potential into practice to aid in determining cellular uptake at the early stages of drug development in any area of research.
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Affiliation(s)
- Rafia Ali
- School of Pharmacy, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
| | - Mehala Balamurali
- Australian Centre for Field Robotics, The University of Sydney, Sydney, NSW 2006, Australia
| | - Pegah Varamini
- School of Pharmacy, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
- The University of Sydney Nano Institute, The University of Sydney, Sydney, NSW 2006, Australia
- Correspondence: ; Tel.: +61-2-86270809
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