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Arab M, Fallah A, Rashidi S, Dastjerdi MM, Ahmadinejad N. Computer-Aided Classification of Breast Lesions Based on US RF Time Series Using a Novel Machine Learning Approach. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024. [PMID: 39140240 DOI: 10.1002/jum.16542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 06/25/2024] [Accepted: 07/21/2024] [Indexed: 08/15/2024]
Abstract
OBJECTIVES One of the most promising adjuncts for screening breast cancer is ultrasound (US) radio-frequency (RF) time series. It has the superiority of not requiring any supplementary equipment over other methods. This research aimed to propound a machine learning (ML) approach for automatically classifying benign, probably benign, suspicious, and malignant breast lesions based on the features extracted from the accumulated US RF time series. METHODS In this article, 220 data of the aforementioned categories, recorded from 118 patients, were analyzed. The dataset, named RFTSBU, was registered by a SuperSonic Imagine Aixplorer medical/research system equipped with a linear transducer. The regions of interest (ROIs) of the B-mode images were manually selected by an expert radiologist before computing the suggested features. Regarding time, frequency, and time-frequency domains, 291 various features were extracted from each ROI. Finally, the features were classified by a pioneering technique named the reference classification method (RCM). Furthermore, the Lee filter was applied to evaluate the effectiveness of reducing speckle noise on the outcomes. RESULTS The accuracy of two-class, three-class, and four-class classifications were respectively calculated 98.59 ± 0.71%, 98.13 ± 0.69%, and 96.10 ± 0.66% (considering 10 repetitions) while support vector machine (SVM) and K-nearest neighbor (KNN) classifiers with 5-fold cross-validation were utilized. CONCLUSIONS This article represented the proposed approach, named CCRFML, to distinguish between breast lesions based on registered in vivo RF time series employing an ML framework. The proposed method's impressive level of classification accuracy attests to its capability of effectively assisting medical professionals in the noninvasive differentiation of breast lesions.
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Affiliation(s)
- Mahsa Arab
- Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Ali Fallah
- Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Saeid Rashidi
- Faculty of Medical Sciences & Technologies, Science & Research Branch, Islamic Azad University, Tehran, Iran
| | | | - Nasrin Ahmadinejad
- Radiology-Medical Imaging Center, Cancer Research Institute, Imam Khomeini Hospital Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences (TUMS), Tehran, Iran
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Nicosia L, Bozzini AC, Pesapane F, Rotili A, Marinucci I, Signorelli G, Frassoni S, Bagnardi V, Origgi D, De Marco P, Abiuso I, Sangalli C, Balestreri N, Corso G, Cassano E. Breast Digital Tomosynthesis versus Contrast-Enhanced Mammography: Comparison of Diagnostic Application and Radiation Dose in a Screening Setting. Cancers (Basel) 2023; 15:cancers15092413. [PMID: 37173880 PMCID: PMC10177523 DOI: 10.3390/cancers15092413] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/15/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
This study aims to evaluate the Average Glandular Dose (AGD) and diagnostic performance of CEM versus Digital Mammography (DM) as well as versus DM plus one-view Digital Breast Tomosynthesis (DBT), which were performed in the same patients at short intervals of time. A preventive screening examination in high-risk asymptomatic patients between 2020 and 2022 was performed with two-view Digital Mammography (DM) projections (Cranio Caudal and Medio Lateral) plus one Digital Breast Tomosynthesis (DBT) projection (mediolateral oblique, MLO) in a single session examination. For all patients in whom we found a suspicious lesion by using DM + DBT, we performed (within two weeks) a CEM examination. AGD and compression force were compared between the diagnostic methods. All lesions identified by DM + DBT were biopsied; then, we assessed whether lesions found by DBT were also highlighted by DM alone and/or by CEM. We enrolled 49 patients with 49 lesions in the study. The median AGD was lower for DM alone than for CEM (3.41 mGy vs. 4.24 mGy, p = 0.015). The AGD for CEM was significantly lower than for the DM plus one single projection DBT protocol (4.24 mGy vs. 5.55 mGy, p < 0.001). We did not find a statistically significant difference in the median compression force between the CEM and DM + DBT. DM + DBT allows the identification of one more invasive neoplasm one in situ lesion and two high-risk lesions, compared to DM alone. The CEM, compared to DM + DBT, failed to identify only one of the high-risk lesions. According to these results, CEM could be used in the screening of asymptomatic high-risk patients.
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Affiliation(s)
- Luca Nicosia
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Anna Carla Bozzini
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Filippo Pesapane
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Anna Rotili
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Irene Marinucci
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Giulia Signorelli
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Samuele Frassoni
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, 20126 Milan, Italy
| | - Vincenzo Bagnardi
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, 20126 Milan, Italy
| | - Daniela Origgi
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy
| | - Paolo De Marco
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy
| | - Ida Abiuso
- Radiology Department, Università Degli Studi di Torino, 10124 Turin, Italy
| | - Claudia Sangalli
- Data Management, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Nicola Balestreri
- Department of Radiology, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Giovanni Corso
- Division of Breast Surgery, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
- European Cancer Prevention Organization, 20122 Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
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Bahl M. Invited Commentary: The Power and Promise of Artificial Intelligence for Digital Breast Tomosynthesis. Radiographics 2023; 43:e220162. [PMID: 36331880 PMCID: PMC9817867 DOI: 10.1148/rg.220162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 07/01/2022] [Indexed: 11/06/2022]
Affiliation(s)
- Manisha Bahl
- From the Department of Radiology, Division of Breast Imaging,
Massachusetts General Hospital, 55 Fruit St, WAC 240, Boston, MA 02114
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Harvey JA. Randomized Controlled Mammography Screening Trials Revisited. JOURNAL OF BREAST IMAGING 2022; 4:105-107. [PMID: 38422431 DOI: 10.1093/jbi/wbac007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Indexed: 03/02/2024]
Affiliation(s)
- Jennifer A Harvey
- University of Rochester Medical Center, Department of Imaging Sciences, Rochester, NY, USA
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