Review
Radiology, Nuclear Medicine & Medical Imaging
Tone Hovda, Kaitlyn Tsuruda, Solveig Roth Hoff, Kristine Kleivi Sahlberg, Solveig Hofvind
Summary: The study reviewed mammograms for 1225 women with screen-detected breast cancer. Results showed that visible findings on prior screening mammograms were not necessarily indicative of screening failure. Some cases were classified as missed, aiming to improve the quality of breast cancer screening.
EUROPEAN RADIOLOGY
(2021)
Article
Oncology
L. Ding, M. J. W. Greuter, I Truyen, M. Goossens, H. De Schutter, G. H. de Bock, G. Van Hal
Summary: The study evaluated the impact of irregular screening behavior on the risk of advanced stage breast cancer in Flanders. The results showed that never attenders were nearly six times more likely to be diagnosed with advanced stage breast cancer compared to regular attenders.
Article
Computer Science, Information Systems
Jinrong Qu, Xuran Zhao, Peng Chen, Zhaoqi Wang, Zhenzhen Liu, Bailin Yang, Hailiang Li
Summary: This study demonstrates that a deep learning classification model trained on historical mammograms can achieve excellent diagnostic performance on new exams, outperforming radiologists in breast cancer diagnosis. The DL model can accurately localize lesions on mammograms even without specific lesion information during training. Furthermore, the impact of input image resolution and different DL model architectures on diagnostic accuracy was analyzed.
MULTIMEDIA SYSTEMS
(2022)
Article
Oncology
Shu Jiang, Debbie L. Bennett, Bernard A. Rosner, Graham A. Colditz
Summary: This study found that the rate of change in breast density was associated with the risk of subsequent breast cancer. Incorporation of longitudinal changes into existing models could optimize risk stratification and guide more personalized risk management.
Article
Engineering, Biomedical
Le Ma, Hui Liu, Xiaojia Lin, Yuxing Cai, Ling Zhang, Weiguo Chen, Genggeng Qin
Summary: This study explored lesion-specific exposure parameters on digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) and evaluated their efficiency for breast cancer diagnosis. The results showed that breast lesion type has an influence on exposure parameters, providing evidence for distinguishing between benign and malignant lesions.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Computer Science, Artificial Intelligence
U. Raghavendra, Anjan Gudigar, Edward J. Ciaccio, Kwan Hoong Ng, Wai Yee Chan, Kartini Rahmat, U. Rajendra Acharya
Summary: Accurate and early detection of breast cancer using effective imaging modalities, such as CAD and BIRADS, is crucial in maintaining patient health. The comparison between 2DSM and FFDM imaging modalities can assist in discriminating BIRADS grades and improving the early detection of breast cancer.
Article
Radiology, Nuclear Medicine & Medical Imaging
Kristin Johnson, Kristina Lang, Debra M. Ikeda, Anna Akesson, Ingvar Andersson, Sophia Zackrisson
Summary: Interval cancer rates were lower in DBT screening compared to DM screening, potentially indicating screening benefits. Interval cancers in the trial generally exhibited nonfavorable characteristics.
Article
Radiology, Nuclear Medicine & Medical Imaging
Ann L. Brown, Charmi Vijapura, Mitva Patel, Alexis De La Cruz, Rifat Wahab
Summary: Dense breast tissue is a strong independent risk factor for breast cancer, with a higher risk compared to fatty breasts. ABUS and MRI are effective in detecting breast cancer, and MRI is especially good at detecting DCIS. Awareness of breast density varies among different ethnic groups, with lower awareness among Asian, Hispanic, Black, and Jewish women compared to White women. Black and Hispanic women are less likely to receive supplemental screening.
Article
Radiology, Nuclear Medicine & Medical Imaging
Mi Young Kim, Young Jin Suh, Yeong Yi An
Summary: This study compared the diagnostic performance of abbreviated breast MRI (AB-MRI) and digital breast tomosynthesis (DBT) in postoperative screening for women with a personal history of breast cancer. The results showed that AB-MRI had an improved cancer detection rate, especially for invasive cancer. AB-MRI demonstrated higher sensitivity and positive predictive value compared to DBT, without sacrificing specificity.
ACADEMIC RADIOLOGY
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Si Eun Lee, Kyunghwa Han, Jung Hyun Yoon, Ji Hyun Youk, Eun-Kyung Kim
Summary: This study evaluates the depiction of breast cancers using artificial intelligence-based computer-assisted diagnosis (AI-CAD). Breast cancers with high abnormality scores are associated with higher BI-RADS category, invasive pathology, and higher cancer stage.
EUROPEAN RADIOLOGY
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Pierpaolo Pattacini, Andrea Nitrosi, Paolo Giorgi Rossi, Stephen W. Duffy, Valentina Iotti, Vladimiro Ginocchi, Sara Ravaioli, Rita Vacondio, Pamela Mancuso, Moira Ragazzi, Cinzia Campari
Summary: The baseline cancer detection was higher in the DBT plus DM arm compared to the DM arm, but the interval cancer incidence was similar between the two groups. Cumulative incidence remained higher in women over 50 in the DBT plus DM arm, while it was similar in women aged 45-49.
Review
Public, Environmental & Occupational Health
N. Moshina, R. S. Falk, S. Hofvind
Summary: This study aimed to explore the long-term quality of life (QoL) among breast cancer survivors eligible for mammographic screening at diagnosis and compare that to QoL among women with no history of breast cancer. The meta-analysis showed no significant differences in QoL for breast cancer survivors compared with women with no history of breast cancer. The findings suggest that there is no significant difference in QoL between breast cancer survivors and women without a history of breast cancer.
Article
Radiology, Nuclear Medicine & Medical Imaging
Jose Luis Raya-Povedano, Sara Romero-Martin, Esperanza Elias-Cabot, Albert Gubern-Merida, Alejandro Rodriguez-Ruiz, Marina Alvarez-Benito
Summary: Using artificial intelligence systems in breast cancer screening can significantly reduce workload without compromising cancer detection rates, according to the study.
Review
Radiology, Nuclear Medicine & Medical Imaging
Raymond J. Acciavatti, Su Hyun Lee, Beatriu Reig, Linda Moy, Emily F. Conant, Despina Kontos, Woo Kyung Moon
Summary: Breast density is an independent risk factor for breast cancer and can be assessed visually using digital mammography and tomosynthesis. In addition to density, textural complexity is also a measure of breast cancer risk. Other screening modalities like breast US and MRI offer independent risk measures as well.
Article
Computer Science, Artificial Intelligence
Saad M. Almutairi, S. Manimurugan, Majed M. Aborokbah, C. Narmatha, Subramaniam Ganesan, P. Karthikeyan
Summary: Breast cancer is the most common disease in women worldwide. Computer-aided detection methods have been used for interpreting and improving the detection of breast cancer. This research focuses on classifying newly generated breast cancer images using a model that performs preprocessing, segmentation, feature extraction, and classification. The model achieved high accuracy and performance on ultrasound and mammogram images.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Article
Tropical Medicine
Guillermo Sanchez, Carlos Gustavo Nino, Carolina Estupinan
Article
Tropical Medicine
Aurelio Mejia, Juan Manuel, Mateo Ceballos, Sara Atehortua, Juan Manuel Toro, Clara Saldarriaga, Maria Elena Mejia, Carolina Ramirez
Article
Radiology, Nuclear Medicine & Medical Imaging
Mindy Y. Licurse, Sung H. Kim, Woojin Kim, Alexander T. Ruutiainen, Tessa S. Cook
JOURNAL OF DIGITAL IMAGING
(2015)
Article
Radiology, Nuclear Medicine & Medical Imaging
Ernest U. Ekpo, Mark F. McEntee
JOURNAL OF DIGITAL IMAGING
(2016)
Article
Radiology, Nuclear Medicine & Medical Imaging
Michael Silosky, Rebecca M. Marsh
JOURNAL OF DIGITAL IMAGING
(2016)
Article
Medicine, General & Internal
Claudia Allemani, Hannah K. Weir, Helena Carreira, Rhea Harewood, Devon Spika, Xiao-Si Wang, Finian Bannon, Jane V. Ahn, Christopher J. Johnson, Audrey Bonaventure, Rafael Marcos-Gragera, Charles Stiller, Gulnar Azevedo e Silva, Wan-Qing Chen, Olufemi J. Ogunbiyi, Bernard Rachet, Matthew J. Soeberg, Hui You, Tomohiro Matsuda, Magdalena Bielska-Lasota, Hans Storm, Thomas C. Tucker, Michel P. Coleman
Article
Radiology, Nuclear Medicine & Medical Imaging
Fiona J. Gilbert, Lorraine Tucker, Ken C. Young
CLINICAL RADIOLOGY
(2016)
Article
Radiology, Nuclear Medicine & Medical Imaging
Jeffrey R. Hawley, Clayton R. Taylor, Alyssa M. Cubbison, B. Selnur Erdal, Vedat O. Yildiz, Selin Carkaci
JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY
(2016)
Article
Radiology, Nuclear Medicine & Medical Imaging
Antonio J. x Salazar, Javier A. Romero, Oscar A. Bernal, Angela P. Moreno, Sofia C. Velasco
JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY
(2017)
Article
Radiology, Nuclear Medicine & Medical Imaging
Antonio J. Salazar, Nicolas Useche, Sonia Bermudez, Anibal Morillo, Oscar Torres, Manuel Granja, Natalia Rueda, Brenda Ropero
JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY
(2019)
Article
Health Care Sciences & Services
Antonio J. Salazar, Nicolas Useche, Manuel Granja, Sonia Bermudez, Anibal J. Morillo, Oscar Torres, Natalia Rueda, Brenda Ropero
Summary: This study aimed to evaluate individual regions of the Alberta Stroke Program Early CT Score in noncontrast head CT interpretations using a smartphone in a telestroke network. The results showed that the smartphone reading system had potential in assessing different regions, but further improvements in reliability and accuracy are needed.
JOURNAL OF TELEMEDICINE AND TELECARE
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Antonio J. Salazar, Nicolas Useche, Sonia Bermudez, Anibal J. Morillo, Oscar Torres, Manuel F. Granja, Natalia Rueda, Brenda Ropero
AMERICAN JOURNAL OF ROENTGENOLOGY
(2020)
Article
Health Care Sciences & Services
Hernan Bayona, Brenda Ropero, Antonio Jose Salazar, Juan Camilo Perez, Manuel Felipe Granja, Carlos Fernando Martinez, Juan Nicolas Useche
JOURNAL OF MEDICAL INTERNET RESEARCH
(2020)
Article
Medicine, General & Internal
Antonio J. Salazar, Nicolas Useche, Manuel F. Granja, Anibal J. Morillo, Sonia Bermudez, Didier Sossa, Claudia J. Ortiz, Oscar J. Torres, Brenda Ropero
Proceedings Paper
Neuroimaging
Victor Manuel Castro, Nestor Andres Munoz, Antonio Jose Salazar
10TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION PROCESSING AND ANALYSIS
(2015)