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Walston SL, Seki H, Takita H, Mitsuyama Y, Sato S, Hagiwara A, Ito R, Hanaoka S, Miki Y, Ueda D. Data set terminology of deep learning in medicine: a historical review and recommendation. Jpn J Radiol 2024:10.1007/s11604-024-01608-1. [PMID: 38856878 DOI: 10.1007/s11604-024-01608-1] [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: 02/29/2024] [Accepted: 05/31/2024] [Indexed: 06/11/2024]
Abstract
Medicine and deep learning-based artificial intelligence (AI) engineering represent two distinct fields each with decades of published history. The current rapid convergence of deep learning and medicine has led to significant advancements, yet it has also introduced ambiguity regarding data set terms common to both fields, potentially leading to miscommunication and methodological discrepancies. This narrative review aims to give historical context for these terms, accentuate the importance of clarity when these terms are used in medical deep learning contexts, and offer solutions to mitigate misunderstandings by readers from either field. Through an examination of historical documents, including articles, writing guidelines, and textbooks, this review traces the divergent evolution of terms for data sets and their impact. Initially, the discordant interpretations of the word 'validation' in medical and AI contexts are explored. We then show that in the medical field as well, terms traditionally used in the deep learning domain are becoming more common, with the data for creating models referred to as the 'training set', the data for tuning of parameters referred to as the 'validation (or tuning) set', and the data for the evaluation of models as the 'test set'. Additionally, the test sets used for model evaluation are classified into internal (random splitting, cross-validation, and leave-one-out) sets and external (temporal and geographic) sets. This review then identifies often misunderstood terms and proposes pragmatic solutions to mitigate terminological confusion in the field of deep learning in medicine. We support the accurate and standardized description of these data sets and the explicit definition of data set splitting terminologies in each publication. These are crucial methods for demonstrating the robustness and generalizability of deep learning applications in medicine. This review aspires to enhance the precision of communication, thereby fostering more effective and transparent research methodologies in this interdisciplinary field.
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Affiliation(s)
- Shannon L Walston
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Hiroshi Seki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Hirotaka Takita
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Yasuhito Mitsuyama
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Shingo Sato
- Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University, Nagoya, Japan
| | - Shouhei Hanaoka
- Department of Radiology, University of Tokyo Hospital, Tokyo, Japan
| | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan.
- Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan.
- Center for Health Science Innovation, Osaka Metropolitan University, Osaka, Japan.
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Qian N, Jiang W, Wu X, Zhang N, Yu H, Guo Y. Lesion attention guided neural network for contrast-enhanced mammography-based biomarker status prediction in breast cancer. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108194. [PMID: 38678959 DOI: 10.1016/j.cmpb.2024.108194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 04/13/2024] [Accepted: 04/21/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND AND OBJECTIVE Accurate identification of molecular biomarker statuses is crucial in cancer diagnosis, treatment, and prognosis. Studies have demonstrated that medical images could be utilized for non-invasive prediction of biomarker statues. The biomarker status-associated features extracted from medical images are essential in developing medical image-based non-invasive prediction models. Contrast-enhanced mammography (CEM) is a promising imaging technique for breast cancer diagnosis. This study aims to develop a neural network-based method to extract biomarker-related image features from CEM images and evaluate the potential of CEM in non-invasive biomarker status prediction. METHODS An end-to-end learning convolutional neural network with the whole breast images as inputs was proposed to extract CEM features for biomarker status prediction in breast cancer. The network focused on lesion regions and flexibly extracted image features from lesion and peri‑tumor regions by employing supervised learning with a smooth L1-based consistency constraint. An image-level weakly supervised segmentation network based on Vision Transformer with cross attention to contrast images of breasts with lesions against the contralateral breast images was developed for automatic lesion segmentation. Finally, prediction models were developed following further selection of significant features and the implementation of random forest-based classification. Results were reported using the area under the curve (AUC), accuracy, sensitivity, and specificity. RESULTS A dataset from 1203 breast cancer patients was utilized to develop and evaluate the proposed method. Compared to the method without lesion attention and with only lesion regions as inputs, the proposed method performed better at biomarker status prediction. Specifically, it achieved an AUC of 0.71 (95 % confidence interval [CI]: 0.65, 0.77) for Ki-67 and 0.73 (95 % CI: 0.65, 0.80) for human epidermal growth factor receptor 2 (HER2). CONCLUSIONS A lesion attention-guided neural network was proposed in this work to extract CEM image features for biomarker status prediction in breast cancer. The promising results demonstrated the potential of CEM in non-invasively predicting the biomarker statuses in breast cancer.
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Affiliation(s)
- Nini Qian
- Department of Biomedical Engineering, Medical School, Tianjin University, Tianjin 300072, China; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
| | - Wei Jiang
- Department of Biomedical Engineering, Medical School, Tianjin University, Tianjin 300072, China; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China; Department of Radiotherapy, Yantai Yuhuangding Hospital, Shandong 264000, China
| | - Xiaoqian Wu
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao 266071, China
| | - Ning Zhang
- Department of Biomedical Engineering, Medical School, Tianjin University, Tianjin 300072, China; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
| | - Hui Yu
- Department of Biomedical Engineering, Medical School, Tianjin University, Tianjin 300072, China; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
| | - Yu Guo
- Department of Biomedical Engineering, Medical School, Tianjin University, Tianjin 300072, China; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China.
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Tsai HY, Kao YW, Wang JC, Tsai TY, Chung WS, Hsu JS, Hou MF, Weng SF. Multitask deep learning on mammography to predict extensive intraductal component in invasive breast cancer. Eur Radiol 2024; 34:2593-2604. [PMID: 37812297 DOI: 10.1007/s00330-023-10254-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/26/2023] [Accepted: 08/07/2023] [Indexed: 10/10/2023]
Abstract
OBJECTIVES To develop a multitask deep learning (DL) algorithm to automatically classify mammography imaging findings and predict the existence of extensive intraductal component (EIC) in invasive breast cancer. METHODS Mammograms with invasive breast cancers from 2010 to 2019 were downloaded for two radiologists performing image segmentation and imaging findings annotation. Images were randomly split into training, validation, and test datasets. A multitask approach was performed on the EfficientNet-B0 neural network mainly to predict EIC and classify imaging findings. Three more models were trained for comparison, including a single-task model (predicting EIC), a two-task model (predicting EIC and cell receptor status), and a three-task model (combining the abovementioned tasks). Additionally, these models were trained in a subgroup of invasive ductal carcinoma. The DeLong test was used to examine the difference in model performance. RESULTS This study enrolled 1459 breast cancers on 3076 images. The EIC-positive rate was 29.0%. The three-task model was the best DL model with an area under the curve (AUC) of EIC prediction of 0.758 and 0.775 at the image and breast (patient) levels, respectively. Mass was the most accurately classified imaging finding (AUC = 0.915), followed by calcifications and mass with calcifications (AUC = 0.878 and 0.824, respectively). Cell receptor status prediction was less accurate (AUC = 0.625-0.653). The multitask approach improves the model training compared to the single-task model, but without significant effects. CONCLUSIONS A mammography-based multitask DL model can perform simultaneous imaging finding classification and EIC prediction. CLINICAL RELEVANCE STATEMENT The study results demonstrated the potential of deep learning to extract more information from mammography for clinical decision-making. KEY POINTS • Extensive intraductal component (EIC) is an independent risk factor of local tumor recurrence after breast-conserving surgery. • A mammography-based deep learning model was trained to predict extensive intraductal component close to radiologists' reading. • The developed multitask deep learning model could perform simultaneous imaging finding classification and extensive intraductal component prediction.
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Affiliation(s)
- Huei-Yi Tsai
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
- Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Center for Big Data Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Yu-Wei Kao
- Department of Healthcare Administration and Medical Informatics, College of Health Science, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Jo-Ching Wang
- Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Tsung-Yu Tsai
- Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Wei-Shiuan Chung
- Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Department of Medical Imaging, Kaohsiung Municipal Siaogang Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Jui-Sheng Hsu
- Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Ming-Feng Hou
- Department of Biomedical Science and Environmental Biology, College of Life Science, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Shih-Feng Weng
- Center for Big Data Research, Kaohsiung Medical University, Kaohsiung, Taiwan.
- Department of Healthcare Administration and Medical Informatics, College of Health Science, Kaohsiung Medical University, Kaohsiung, Taiwan.
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
- Center for Medical Informatics and Statistics, Office of R&D, Kaohsiung Medical University, Kaohsiung, Taiwan.
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Ueda D, Kakinuma T, Fujita S, Kamagata K, Fushimi Y, Ito R, Matsui Y, Nozaki T, Nakaura T, Fujima N, Tatsugami F, Yanagawa M, Hirata K, Yamada A, Tsuboyama T, Kawamura M, Fujioka T, Naganawa S. Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol 2024; 42:3-15. [PMID: 37540463 PMCID: PMC10764412 DOI: 10.1007/s11604-023-01474-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 07/17/2023] [Indexed: 08/05/2023]
Abstract
In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.
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Affiliation(s)
- Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-Machi, Abeno-ku, Osaka, 545-8585, Japan.
| | | | - Shohei Fujita
- Department of Radiology, University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Sakyoku, Kyoto, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kita-ku, Okayama, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Chuo-ku, Kumamoto, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, Minami-ku, Hiroshima, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita-ku, Sapporo, Hokkaido, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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Kinoshita M, Ueda D, Matsumoto T, Shinkawa H, Yamamoto A, Shiba M, Okada T, Tani N, Tanaka S, Kimura K, Ohira G, Nishio K, Tauchi J, Kubo S, Ishizawa T. Deep Learning Model Based on Contrast-Enhanced Computed Tomography Imaging to Predict Postoperative Early Recurrence after the Curative Resection of a Solitary Hepatocellular Carcinoma. Cancers (Basel) 2023; 15:cancers15072140. [PMID: 37046801 PMCID: PMC10092973 DOI: 10.3390/cancers15072140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/28/2023] [Accepted: 04/03/2023] [Indexed: 04/08/2023] Open
Abstract
We aimed to develop the deep learning (DL) predictive model for postoperative early recurrence (within 2 years) of hepatocellular carcinoma (HCC) based on contrast-enhanced computed tomography (CECT) imaging. This study included 543 patients who underwent initial hepatectomy for HCC and were randomly classified into training, validation, and test datasets at a ratio of 8:1:1. Several clinical variables and arterial CECT images were used to create predictive models for early recurrence. Artificial intelligence models were implemented using convolutional neural networks and multilayer perceptron as a classifier. Furthermore, the Youden index was used to discriminate between high- and low-risk groups. The importance values of each explanatory variable for early recurrence were calculated using permutation importance. The DL predictive model for postoperative early recurrence was developed with the area under the curve values of 0.71 (test datasets) and 0.73 (validation datasets). Postoperative early recurrence incidences in the high- and low-risk groups were 73% and 30%, respectively (p = 0.0057). Permutation importance demonstrated that among the explanatory variables, the variable with the highest importance value was CECT imaging analysis. We developed a DL model to predict postoperative early HCC recurrence. DL-based analysis is effective for determining the treatment strategies in patients with HCC.
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Affiliation(s)
- Masahiko Kinoshita
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Daiju Ueda
- Smart Life Science Lab, Center for Health Science Innovation, Osaka Metropolitan University, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
- Department of Diagnostic and Interventional Radiology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Toshimasa Matsumoto
- Smart Life Science Lab, Center for Health Science Innovation, Osaka Metropolitan University, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
- Department of Diagnostic and Interventional Radiology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Hiroji Shinkawa
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Akira Yamamoto
- Department of Diagnostic and Interventional Radiology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Masatsugu Shiba
- Smart Life Science Lab, Center for Health Science Innovation, Osaka Metropolitan University, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
- Department of Biofunctional Analysis, Graduate School of medicine, Osaka Metropolitan University, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Takuma Okada
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Naoki Tani
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Shogo Tanaka
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Kenjiro Kimura
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Go Ohira
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Kohei Nishio
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Jun Tauchi
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Shoji Kubo
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Takeaki Ishizawa
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
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Yaneva G, Dimitrova T, Ivanov D, Ingilizova G, Slavov S. Immunohistochemical Marker Patterns in Female Breast Cancer. Open Access Maced J Med Sci 2022. [DOI: 10.3889/oamjms.2022.8950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND: Breast cancer (BC) represents the most common cancer in women worldwide and in Bulgaria. Its great medico-social importance determines the intensive complex research devoted to BC prevention, early diagnosis and management.
AIM: The objective of the present investigation is to reveal some essential peculiarities of four main immunohistochemical markers used in the diagnosis of molecular subtypes of female BC.
MATERIALS AND METHODS: During the period between December 1, 2017 and November 30, 2020, we examined a total of 128 randomly selected female BC patients operated on in Marko Markov Specialized Hospital for Active Treatment of Oncological Diseases of Varna, Bulgaria. We analyze BC molecular types and four immunohistochemical markers in BC patients. The expression of estrogen (ER) and progesterone (PR) receptors is assessed in mammary gland biopsies and surgical specimens by using the indirect immunoperoxidase method with EnVision™ FLEX MiniKit, that of HER2 with HercepTest™ and that of Ki-67 proliferation index with Leica Aperio Scan Scope AT2 device. The positivity and negativity of these receptors in single molecular subtypes is evaluated.
RESULTS: The luminal B HER2-positive and the luminal B HER2-negative subtypes are most common - in 36.72% and 35.16% of the cases, respectively. TNBC subtype is established in 11.72%) the luminal A - in 8.59% and the non-luminal HER2-positive subtype - in 7.81% of the cases. The positive expression is statistically significantly more common in ER (t=8.972; p<0.0001) and PR (t=2.828; p<0.01), while the negative expression insignificantly prevails in HER2.
CONCLUSION: Our immunohistochemical results in female BC patients prove the role of single receptor expression for the proper and timely decision making about the necessity and benefit of additional chemotherapy in selected surgically treated cases.
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Ueda D, Yamamoto A, Onoda N, Takashima T, Noda S, Kashiwagi S, Morisaki T, Fukumoto S, Shiba M, Morimura M, Shimono T, Kageyama K, Tatekawa H, Murai K, Honjo T, Shimazaki A, Kabata D, Miki Y. Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets. PLoS One 2022; 17:e0265751. [PMID: 35324962 PMCID: PMC8947392 DOI: 10.1371/journal.pone.0265751] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 03/07/2022] [Indexed: 12/24/2022] Open
Abstract
Objectives The objective of this study was to develop and validate a state-of-the-art, deep learning (DL)-based model for detecting breast cancers on mammography. Methods Mammograms in a hospital development dataset, a hospital test dataset, and a clinic test dataset were retrospectively collected from January 2006 through December 2017 in Osaka City University Hospital and Medcity21 Clinic. The hospital development dataset and a publicly available digital database for screening mammography (DDSM) dataset were used to train and to validate the RetinaNet, one type of DL-based model, with five-fold cross-validation. The model’s sensitivity and mean false positive indications per image (mFPI) and partial area under the curve (AUC) with 1.0 mFPI for both test datasets were externally assessed with the test datasets. Results The hospital development dataset, hospital test dataset, clinic test dataset, and DDSM development dataset included a total of 3179 images (1448 malignant images), 491 images (225 malignant images), 2821 images (37 malignant images), and 1457 malignant images, respectively. The proposed model detected all cancers with a 0.45–0.47 mFPI and had partial AUCs of 0.93 in both test datasets. Conclusions The DL-based model developed for this study was able to detect all breast cancers with a very low mFPI. Our DL-based model achieved the highest performance to date, which might lead to improved diagnosis for breast cancer.
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Affiliation(s)
- Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
- * E-mail:
| | - Akira Yamamoto
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Naoyoshi Onoda
- Department of Breast and Endocrine Surgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Tsutomu Takashima
- Department of Breast and Endocrine Surgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Satoru Noda
- Department of Breast and Endocrine Surgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Shinichiro Kashiwagi
- Department of Breast and Endocrine Surgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Tamami Morisaki
- Department of Breast and Endocrine Surgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
- Department of Premier Preventive Medicine, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Shinya Fukumoto
- Department of Premier Preventive Medicine, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Masatsugu Shiba
- Department of Gastroenterology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Mina Morimura
- Department of General Practice, Osaka City University Hospital, Osaka, Japan
| | - Taro Shimono
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Ken Kageyama
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Hiroyuki Tatekawa
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Kazuki Murai
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Takashi Honjo
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Akitoshi Shimazaki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Daijiro Kabata
- Department of Medical Statistics, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
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