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Qi L, Lu X, Shen H, Gao Q, Han Z, Zhu J, Meng Y, Wang L, Chen S, Li Y. Automatic Classification of Mass Shape and Margin on Mammography with Artificial Intelligence: Deep CNN Versus Radiomics. J Digit Imaging 2023; 36:1314-1322. [PMID: 36932250 PMCID: PMC10406738 DOI: 10.1007/s10278-023-00798-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 02/16/2023] [Accepted: 02/19/2023] [Indexed: 03/19/2023] Open
Abstract
The purpose of this study is to test the feasibility for deep CNN-based artificial intelligence methods for automatic classification of the mass margin and shape, while radiomic feature-based machine learning methods were also implemented in this study as baseline and for comparison study. In this retrospective study, 596 patients with breast mass that underwent mammography from 4 hospitals were enrolled from January 2012 to October 2019. Margin and shape of each mass were annotated according to BI-RADS by 2 experienced radiologists. Deep CNN-based AI was implemented for the classification task based on Resnet50. Balanced sampler and CBAM were also used to improve the performance of the Deep CNNs. As comparison, image texture features were extracted and then dimensionality reduction methods (such as PCA, ICA) and classical classifiers (such as SVM, DT, KNN) were used for classification task. Based on Python programming software, accuracy (ACC) was used to evaluate the performance of the model, and the model with the highest ACC value was selected. Deep CNN based on Resnet50 with balanced sampler and CBAM achieved the best performance for both margin and shape classification, with ACC of 0.838 and 0.874, respectively. For the radiomics-based machine learning, the best performance for margin was achieved as 0.676 by the combination of FA + RF, while the best performance for shape was 0.802 by the combination of PCA + MLP. The feasibility for automatic classification with coarse labeling of the mass shape and margin was testified with the deep CNN-based AI methods, while radiomic feature-based machine learning methods achieved inferior classification results.
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Affiliation(s)
- Longxiu Qi
- Department of Radiology, The First Affiliated Hospital of Soochow University, Jiangsu Province, Jiangsu Province, 215006, Suzhou City, China
- Department of Radiology, The First People's Hospital of Yancheng, Yancheng City, Jiangsu Province, China
| | - Xing Lu
- Sanmed Biotech Inc, Zhuhai City, Guangzhou Province, China
| | - Hailin Shen
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Jiangsu Province, 215028, Suzhou City, China
| | - Qilei Gao
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu Province, China
| | - Zhigang Han
- Department of Radiology, Huaian Maternal and Child Health Hospital, Huaian City, Jiangsu Province, China
| | - Jianguo Zhu
- Department of Radiology, Xinghai Hospital of Suzhou Industry Park, Suzhou City, Jiangsu Province, China
| | - You Meng
- Department of Breast Surgery, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Linhua Wang
- Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou City, Jiangsu Province, China.
| | - Shuangqing Chen
- Department of Radiology, the Affiliated Suzhou Hospital of Nanjing Medical University, Jiangsu Province, 215001, Suzhou City, China.
| | - Yonggang Li
- Department of Radiology, The First Affiliated Hospital of Soochow University, Jiangsu Province, Jiangsu Province, 215006, Suzhou City, China.
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Wong DJ, Gandomkar Z, Lewis S, Reed W, Suleiman M, Siviengphanom S, Ekpo E. Do Reader Characteristics Affect Diagnostic Efficacy in Screening Mammography? A Systematic Review. Clin Breast Cancer 2023; 23:e56-e67. [PMID: 36792458 DOI: 10.1016/j.clbc.2023.01.009] [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: 04/05/2022] [Revised: 01/10/2023] [Accepted: 01/21/2023] [Indexed: 01/27/2023]
Abstract
To examine reader characteristics associated with diagnostic efficacy in the interpretation of screening mammograms. A systematic search of the literature was conducted using databases such as Cochrane, Scopus, Medline, Embase, Web of Science, and PubMed. Search terms were combined with "AND" or "OR" and included: "Radiologist's characteristics AND performance"; "radiologist experience AND screening mammography"; "annual volume read AND diagnostic efficacy"; "screening mammography performance OR diagnostic efficacy". Studies were included if they assessed reader performance in screening mammography interpretation, breast readers, used a reference standard to assess the performance, and were published in the English language. Twenty-eight studies were reviewed. Increasing reader's age was associated with lower false positive rates. No association was found between gender and performance. Half of the studies showed no association between years of reading mammograms and performance. Most studies showed that high reading volume was more likely to be associated with increased sensitivity, cancer detection rates (CDR), lower recall rate, and lower false positive rates. Inconsistent associations were found between fellowship training in breast imaging and reader performance. Specialization in breast imaging was associated with better CDR, sensitivity, and specificity. Limited studies were available to establish the association between performance and factors such as time spent in breast imaging (n = 2), screening focus (n = 1), formal rotation in mammography (n = 1), owner of practice (n = 1), and practice type (n = 1). No individual characteristics is associated with versatility in diagnostic efficacy, albeit reading volume and specialization in breast imaging appear to be associated with with increased sensitivity and CDR without significantly affecting other performance metrics.
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Affiliation(s)
- Dennis Jay Wong
- Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Imaging Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
| | - Ziba Gandomkar
- Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Imaging Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
| | - Sarah Lewis
- Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Imaging Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
| | - Warren Reed
- Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Imaging Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
| | - Mo'ayyad Suleiman
- Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Imaging Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
| | - Somphone Siviengphanom
- Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Imaging Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
| | - Ernest Ekpo
- Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Imaging Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia.
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Razali NF, Isa IS, Sulaiman SN, Abdul Karim NK, Osman MK, Che Soh ZH. Enhancement Technique Based on the Breast Density Level for Mammogram for Computer-Aided Diagnosis. Bioengineering (Basel) 2023; 10:bioengineering10020153. [PMID: 36829647 PMCID: PMC9952042 DOI: 10.3390/bioengineering10020153] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/04/2023] [Accepted: 01/16/2023] [Indexed: 01/26/2023] Open
Abstract
Mass detection in mammograms has a limited approach to the presence of a mass in overlapping denser fibroglandular breast regions. In addition, various breast density levels could decrease the learning system's ability to extract sufficient feature descriptors and may result in lower accuracy performance. Therefore, this study is proposing a textural-based image enhancement technique named Spatial-based Breast Density Enhancement for Mass Detection (SbBDEM) to boost textural features of the overlapped mass region based on the breast density level. This approach determines the optimal exposure threshold of the images' lower contrast limit and optimizes the parameters by selecting the best intensity factor guided by the best Blind/Reference-less Image Spatial Quality Evaluator (BRISQUE) scores separately for both dense and non-dense breast classes prior to training. Meanwhile, a modified You Only Look Once v3 (YOLOv3) architecture is employed for mass detection by specifically assigning an extra number of higher-valued anchor boxes to the shallower detection head using the enhanced image. The experimental results show that the use of SbBDEM prior to training mass detection promotes superior performance with an increase in mean Average Precision (mAP) of 17.24% improvement over the non-enhanced trained image for mass detection, mass segmentation of 94.41% accuracy, and 96% accuracy for benign and malignant mass classification. Enhancing the mammogram images based on breast density is proven to increase the overall system's performance and can aid in an improved clinical diagnosis process.
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Affiliation(s)
- Noor Fadzilah Razali
- Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Bukit Mertajam 13500, Pulau Pinang, Malaysia
| | - Iza Sazanita Isa
- Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Bukit Mertajam 13500, Pulau Pinang, Malaysia
- Correspondence:
| | - Siti Noraini Sulaiman
- Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Bukit Mertajam 13500, Pulau Pinang, Malaysia
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA Cawangan Selangor, Puncak Alam Campus, Puncak Alam 42300, Selangor, Malaysia
| | - Noor Khairiah Abdul Karim
- Department of Biomedical Imaging, Advanced Medical and Dental Institute, Universiti Sains Malaysia Bertam, Kepala Batas 13200, Pulau Pinang, Malaysia
- Breast Cancer Translational Research Programme (BCTRP), Advanced Medical and Dental Institute, Universiti Sains Malaysia Bertam, Kepala Batas 13200, Pulau Pinang, Malaysia
| | - Muhammad Khusairi Osman
- Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Bukit Mertajam 13500, Pulau Pinang, Malaysia
| | - Zainal Hisham Che Soh
- Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Bukit Mertajam 13500, Pulau Pinang, Malaysia
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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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Improving radiologist's ability in identifying particular abnormal lesions on mammograms through training test set with immediate feedback. Sci Rep 2021; 11:9899. [PMID: 33972611 PMCID: PMC8110801 DOI: 10.1038/s41598-021-89214-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 04/06/2021] [Indexed: 12/24/2022] Open
Abstract
It has been shown that there are differences in diagnostic accuracy of cancer detection on mammograms, from below 50% in developing countries to over 80% in developed world. One previous study reported that radiologists from a population in Asia displayed a low mammographic cancer detection of 48% compared with over 80% in developed countries, and more importantly, that most lesions missed by these radiologists were spiculated masses or stellate lesions. The aim of this study was to explore the performance of radiologists after undertaking a training test set which had been designed to improve the capability in detecting a specific type of cancers on mammograms. Twenty-five radiologists read two sets of 60 mammograms in a standardized mammogram reading room. The first test set focused on stellate or spiculated masses. When radiologists completed the first set, the system displayed immediate feedback to the readers comparing their performances in each case with the truth of cancer cases and cancer types so that the readers could identify individual-based errors. Later radiologists were asked to read the second set of mammograms which contained different types of cancers including stellate/spiculated masses, asymmetric density, calcification, discrete mass and architectural distortion. Case sensitivity, lesion sensitivity, specificity, receiver operating characteristics (ROC) and Jackknife alternative free-response receiver operating characteristics (JAFROC) were calculated for each participant and their diagnostic accuracy was compared between two sessions. Results showed significant improvement among radiologists in case sensitivity (+ 11.4%; P < 0.05), lesion sensitivity (+ 18.7%; P < 0.01) and JAFROC (+ 11%; P < 0.01) in the second set compared with the first set. The increase in diagnostic accuracy was also recorded in the detection of stellate/spiculated mass (+ 20.6%; P < 0.05). This indicated that the performance of radiologists in detecting malignant lesions on mammograms can be improved if an appropriate training intervention is applied after the readers' weakness and strength are identified.
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Vasey B, Ursprung S, Beddoe B, Taylor EH, Marlow N, Bilbro N, Watkinson P, McCulloch P. Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review. JAMA Netw Open 2021; 4:e211276. [PMID: 33704476 PMCID: PMC7953308 DOI: 10.1001/jamanetworkopen.2021.1276] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
IMPORTANCE An increasing number of machine learning (ML)-based clinical decision support systems (CDSSs) are described in the medical literature, but this research focuses almost entirely on comparing CDSS directly with clinicians (human vs computer). Little is known about the outcomes of these systems when used as adjuncts to human decision-making (human vs human with computer). OBJECTIVES To conduct a systematic review to investigate the association between the interactive use of ML-based diagnostic CDSSs and clinician performance and to examine the extent of the CDSSs' human factors evaluation. EVIDENCE REVIEW A search of MEDLINE, Embase, PsycINFO, and grey literature was conducted for the period between January 1, 2010, and May 31, 2019. Peer-reviewed studies published in English comparing human clinician performance with and without interactive use of an ML-based diagnostic CDSSs were included. All metrics used to assess human performance were considered as outcomes. The risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Risk of Bias in Non-Randomised Studies-Intervention (ROBINS-I). Narrative summaries were produced for the main outcomes. Given the heterogeneity of medical conditions, outcomes of interest, and evaluation metrics, no meta-analysis was performed. FINDINGS A total of 8112 studies were initially retrieved and 5154 abstracts were screened; of these, 37 studies met the inclusion criteria. The median number of participating clinicians was 4 (interquartile range, 3-8). Of the 107 results that reported statistical significance, 54 (50%) were increased by the use of CDSSs, 4 (4%) were decreased, and 49 (46%) showed no change or an unclear change. In the subgroup of studies carried out in representative clinical settings, no association between the use of ML-based diagnostic CDSSs and improved clinician performance could be observed. Interobserver agreement was the commonly reported outcome whose change was the most strongly associated with CDSS use. Four studies (11%) reported on user feedback, and, in all but 1 case, clinicians decided to override at least some of the algorithms' recommendations. Twenty-eight studies (76%) were rated as having a high risk of bias in at least 1 of the 4 QUADAS-2 core domains, and 6 studies (16%) were considered to be at serious or critical risk of bias using ROBINS-I. CONCLUSIONS AND RELEVANCE This systematic review found only sparse evidence that the use of ML-based CDSSs is associated with improved clinician diagnostic performance. Most studies had a low number of participants, were at high or unclear risk of bias, and showed little or no consideration for human factors. Caution should be exercised when estimating the current potential of ML to improve human diagnostic performance, and more comprehensive evaluation should be conducted before deploying ML-based CDSSs in clinical settings. The results highlight the importance of considering supported human decisions as end points rather than merely the stand-alone CDSSs outputs.
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Affiliation(s)
- Baptiste Vasey
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
| | - Stephan Ursprung
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
| | - Benjamin Beddoe
- Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Elliott H. Taylor
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
| | - Neale Marlow
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Nicole Bilbro
- Department of Surgery, Maimonides Medical Center, Brooklyn, New York
| | - Peter Watkinson
- Critical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Peter McCulloch
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
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Improved Cancer Detection Using Artificial Intelligence: a Retrospective Evaluation of Missed Cancers on Mammography. J Digit Imaging 2020; 32:625-637. [PMID: 31011956 PMCID: PMC6646649 DOI: 10.1007/s10278-019-00192-5] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
To determine whether cmAssist™, an artificial intelligence-based computer-aided detection (AI-CAD) algorithm, can be used to improve radiologists’ sensitivity in breast cancer screening and detection. A blinded retrospective study was performed with a panel of seven radiologists using a cancer-enriched data set from 122 patients that included 90 false-negative mammograms obtained up to 5.8 years prior to diagnosis and 32 BIRADS 1 and 2 patients with a 2-year follow-up of negative diagnosis. The mammograms were performed between February 7, 2008 (earliest) and January 8, 2016 (latest), and were all originally interpreted as negative in conjunction with R2 ImageChecker CAD, version 10.0. In this study, the readers analyzed the 122 studies before and after review of cmAssist™, an AI-CAD software for mammography. The statistical significance of our findings was evaluated using Student’s t test and bootstrap statistical analysis. There was a substantial and significant improvement in radiologist accuracy with use of cmAssist, as demonstrated in the 7.2% increase in the area-under-the-curve (AUC) of the receiver operating characteristic (ROC) curve with two-sided p value < 0.01 for the reader group. All radiologists showed a significant improvement in their cancer detection rate (CDR) with the use of cmAssist (two-sided p value = 0.030, confidence interval = 95%). The readers detected between 25 and 71% (mean 51%) of the early cancers without assistance. With cmAssist, the overall reader CDR was 41 to 76% (mean 62%). The percentage increase in CDR for the reader panel was significant, ranging from 6 to 64% (mean 27%) with the use of cmAssist. There was less than 1% increase in the readers’ false-positive recalls with use of cmAssist. With the use of cmAssist TM, there was a substantial and statistically significant improvement in radiologists’ accuracy and sensitivity for detection of cancers that were originally missed. The percentage increase in CDR for the radiologists in the reader panel ranged from 6 to 64% (mean 27%) with the use of cmAssist, with negligible increase in false-positive recalls.
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Masud R, Al-Rei M, Lokker C. Computer-Aided Detection for Breast Cancer Screening in Clinical Settings: Scoping Review. JMIR Med Inform 2019; 7:e12660. [PMID: 31322128 PMCID: PMC6670274 DOI: 10.2196/12660] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 05/21/2019] [Accepted: 06/10/2019] [Indexed: 11/14/2022] Open
Abstract
Background With the growth of machine learning applications, the practice of medicine is evolving. Computer-aided detection (CAD) is a software technology that has become widespread in radiology practices, particularly in breast cancer screening for improving detection rates at earlier stages. Many studies have investigated the diagnostic accuracy of CAD, but its implementation in clinical settings has been largely overlooked. Objective The aim of this scoping review was to summarize recent literature on the adoption and implementation of CAD during breast cancer screening by radiologists and to describe barriers and facilitators for CAD use. Methods The MEDLINE database was searched for English, peer-reviewed articles that described CAD implementation, including barriers or facilitators, in breast cancer screening and were published between January 2010 and March 2018. Articles describing the diagnostic accuracy of CAD for breast cancer detection were excluded. The search returned 526 citations, which were reviewed in duplicate through abstract and full-text screening. Reference lists and cited references in the included studies were reviewed. Results A total of nine articles met the inclusion criteria. The included articles showed that there is a tradeoff between the facilitators and barriers for CAD use. Facilitators for CAD use were improved breast cancer detection rates, increased profitability of breast imaging, and time saved by replacing double reading. Identified barriers were less favorable perceptions of CAD compared to double reading by radiologists, an increase in recall rates of patients for further testing, increased costs, and unclear effect on patient outcomes. Conclusions There is a gap in the literature between CAD’s well-established diagnostic accuracy and its implementation and use by radiologists. Generally, the perceptions of radiologists have not been considered and details of implementation approaches for adoption of CAD have not been reported. The cost-effectiveness of CAD has not been well established for breast cancer screening in various populations. Further research is needed on how to best facilitate CAD in radiology practices in order to optimize patient outcomes, and the views of radiologists need to be better considered when advancing CAD use.
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Affiliation(s)
- Rafia Masud
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Mona Al-Rei
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Cynthia Lokker
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
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Jackson RL, Double CR, Munro HJ, Lynch J, Tapia KA, Trieu PD, Alakhras M, Ganesan A, Do TD, Soh BP, Brennan PC, Puslednik P. Breast Cancer Diagnostic Efficacy in a Developing South-East Asian Country. Asian Pac J Cancer Prev 2019; 20:727-731. [PMID: 30909671 PMCID: PMC6825776 DOI: 10.31557/apjcp.2019.20.3.727] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Background: Breast cancer, is increasing in prevalence amongst South East (SE) Asian women, highlighting the
need for high quality, early diagnoses. This study investigated radiologists’ detection efficacy in a developing (DC)
and developed (DDC) SE Asian country, as compared to Australian radiologists. Methods: Using a test-set of 60
mammographic cases, 20 containing cancer, JAFROC figures of merit (FOM) and ROC area under the curves (AUC)
were calculated as well as location sensitivity, sensitivity and specificity. The test set was examined by 35, 15, and
53 radiologists from DC, a DDC and Australia, respectively. Results: DC radiologists, compared to both groups of
counterparts, demonstrated significantly lower JAFROC FOM, ROC AUC and specificity scores. DC radiologists had
a significantly lower location sensitivity than Australian radiologists. DC radiologists also demonstrated significantly
lower values for age, hours of reading per week, and years of mammography experience when compared with other
radiologists. Conclusion: Significant differences in breast cancer detection parameters can be attributed to the experience
of DC radiologists. The development of inexpensive, innovative, interactive training programs are discussed. This nonuniform
level of breast cancer detection between countries must be addressed to achieve the World Health Organisation
goal of health equity.
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Affiliation(s)
| | - Callan R Double
- St Matthews Catholic School, Mudgee, New South Wales, Australia.
| | - Hayden J Munro
- St Matthews Catholic School, Mudgee, New South Wales, Australia.
| | - Jessica Lynch
- St Matthews Catholic School, Mudgee, New South Wales, Australia.
| | - Kriscia A Tapia
- Faculty of Health Sciences, The University of Sydney, Australia
| | - Phuong Dung Trieu
- Faculty of Health Sciences, The University of Sydney, Australia.,Department of Medical Imaging, Ho Chi Minh City University of Medicine and Pharmacy, Vietnam
| | - Maram Alakhras
- Faculty of Health Sciences, The University of Sydney, Australia
| | - Aarthi Ganesan
- Faculty of Health Sciences, The University of Sydney, Australia
| | - Thuan Doan Do
- Department of Diagnostic Imaging, Vietnam National Cancer Hospital, Vietnam
| | | | | | - Puslednik Puslednik
- St Matthews Catholic School, Mudgee, New South Wales, Australia. ,Faculty of Health Sciences, The University of Sydney, Australia
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11
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Henriksen EL, Carlsen JF, Vejborg IMM, Nielsen MB, Lauridsen CA. The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review. Acta Radiol 2019; 60:13-18. [PMID: 29665706 DOI: 10.1177/0284185118770917] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Early detection of breast cancer (BC) is crucial in lowering the mortality. PURPOSE To present an overview of studies concerning computer-aided detection (CAD) in screening mammography for early detection of BC and compare diagnostic accuracy and recall rates (RR) of single reading (SR) with SR + CAD and double reading (DR) with SR + CAD. MATERIAL AND METHODS PRISMA guidelines were used as a review protocol. Articles on clinical trials concerning CAD for detection of BC in a screening population were included. The literature search resulted in 1522 records. A total of 1491 records were excluded by abstract and 18 were excluded by full text reading. A total of 13 articles were included. RESULTS All but two studies from the SR vs. SR + CAD group showed an increased sensitivity and/or cancer detection rate (CDR) when adding CAD. The DR vs. SR + CAD group showed no significant differences in sensitivity and CDR. Adding CAD to SR increased the RR and decreased the specificity in all but one study. For the DR vs. SR + CAD group only one study reported a significant difference in RR. CONCLUSION All but two studies showed an increase in RR, sensitivity and CDR when adding CAD to SR. Compared to DR no statistically significant differences in sensitivity or CDR were reported. Additional studies based on organized population-based screening programs, with longer follow-up time, high-volume readers, and digital mammography are needed to evaluate the efficacy of CAD.
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Affiliation(s)
- Emilie L Henriksen
- Department of Diagnostic Radiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of technology, Faculty of Health and Technology, Metropolitan University College, Copenhagen, Denmark
| | - Jonathan F Carlsen
- Department of Diagnostic Radiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Ilse MM Vejborg
- Department of Diagnostic Radiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Michael B Nielsen
- Department of Diagnostic Radiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Carsten A Lauridsen
- Department of Diagnostic Radiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of technology, Faculty of Health and Technology, Metropolitan University College, Copenhagen, Denmark
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Rodríguez-Ruiz A, Krupinski E, Mordang JJ, Schilling K, Heywang-Köbrunner SH, Sechopoulos I, Mann RM. Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System. Radiology 2018; 290:305-314. [PMID: 30457482 DOI: 10.1148/radiol.2018181371] [Citation(s) in RCA: 260] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Purpose To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial intelligence (AI) system. Materials and Methods An enriched retrospective, fully crossed, multireader, multicase, HIPAA-compliant study was performed. Screening digital mammographic examinations from 240 women (median age, 62 years; range, 39-89 years) performed between 2013 and 2017 were included. The 240 examinations (100 showing cancers, 40 leading to false-positive recalls, 100 normal) were interpreted by 14 Mammography Quality Standards Act-qualified radiologists, once with and once without AI support. The readers provided a Breast Imaging Reporting and Data System score and probability of malignancy. AI support provided radiologists with interactive decision support (clicking on a breast region yields a local cancer likelihood score), traditional lesion markers for computer-detected abnormalities, and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), specificity and sensitivity, and reading time were compared between conditions by using mixed-models analysis dof variance and generalized linear models for multiple repeated measurements. Results On average, the AUC was higher with AI support than with unaided reading (0.89 vs 0.87, respectively; P = .002). Sensitivity increased with AI support (86% [86 of 100] vs 83% [83 of 100]; P = .046), whereas specificity trended toward improvement (79% [111 of 140]) vs 77% [108 of 140]; P = .06). Reading time per case was similar (unaided, 146 seconds; supported by AI, 149 seconds; P = .15). The AUC with the AI system alone was similar to the average AUC of the radiologists (0.89 vs 0.87). Conclusion Radiologists improved their cancer detection at mammography when using an artificial intelligence system for support, without requiring additional reading time. Published under a CC BY 4.0 license. See also the editorial by Bahl in this issue.
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Affiliation(s)
- Alejandro Rodríguez-Ruiz
- From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (A.R.R., I.S., R.M.M.); Department of Radiology & Imaging Sciences, Emory University, Atlanta, Ga (E.K.); ScreenPoint Medical BV, Nijmegen, the Netherlands (J.J.M.); Lynn Women's Health & Wellness Institute, Boca Raton Regional Hospital, Boca Raton, Fla (K.S.); Referenzzentrum Mammographie Munich, Brustdiagnostik München and FFB, Munich, Germany (S.H.H.); and Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Elizabeth Krupinski
- From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (A.R.R., I.S., R.M.M.); Department of Radiology & Imaging Sciences, Emory University, Atlanta, Ga (E.K.); ScreenPoint Medical BV, Nijmegen, the Netherlands (J.J.M.); Lynn Women's Health & Wellness Institute, Boca Raton Regional Hospital, Boca Raton, Fla (K.S.); Referenzzentrum Mammographie Munich, Brustdiagnostik München and FFB, Munich, Germany (S.H.H.); and Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Jan-Jurre Mordang
- From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (A.R.R., I.S., R.M.M.); Department of Radiology & Imaging Sciences, Emory University, Atlanta, Ga (E.K.); ScreenPoint Medical BV, Nijmegen, the Netherlands (J.J.M.); Lynn Women's Health & Wellness Institute, Boca Raton Regional Hospital, Boca Raton, Fla (K.S.); Referenzzentrum Mammographie Munich, Brustdiagnostik München and FFB, Munich, Germany (S.H.H.); and Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Kathy Schilling
- From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (A.R.R., I.S., R.M.M.); Department of Radiology & Imaging Sciences, Emory University, Atlanta, Ga (E.K.); ScreenPoint Medical BV, Nijmegen, the Netherlands (J.J.M.); Lynn Women's Health & Wellness Institute, Boca Raton Regional Hospital, Boca Raton, Fla (K.S.); Referenzzentrum Mammographie Munich, Brustdiagnostik München and FFB, Munich, Germany (S.H.H.); and Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Sylvia H Heywang-Köbrunner
- From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (A.R.R., I.S., R.M.M.); Department of Radiology & Imaging Sciences, Emory University, Atlanta, Ga (E.K.); ScreenPoint Medical BV, Nijmegen, the Netherlands (J.J.M.); Lynn Women's Health & Wellness Institute, Boca Raton Regional Hospital, Boca Raton, Fla (K.S.); Referenzzentrum Mammographie Munich, Brustdiagnostik München and FFB, Munich, Germany (S.H.H.); and Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Ioannis Sechopoulos
- From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (A.R.R., I.S., R.M.M.); Department of Radiology & Imaging Sciences, Emory University, Atlanta, Ga (E.K.); ScreenPoint Medical BV, Nijmegen, the Netherlands (J.J.M.); Lynn Women's Health & Wellness Institute, Boca Raton Regional Hospital, Boca Raton, Fla (K.S.); Referenzzentrum Mammographie Munich, Brustdiagnostik München and FFB, Munich, Germany (S.H.H.); and Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Ritse M Mann
- From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (A.R.R., I.S., R.M.M.); Department of Radiology & Imaging Sciences, Emory University, Atlanta, Ga (E.K.); ScreenPoint Medical BV, Nijmegen, the Netherlands (J.J.M.); Lynn Women's Health & Wellness Institute, Boca Raton Regional Hospital, Boca Raton, Fla (K.S.); Referenzzentrum Mammographie Munich, Brustdiagnostik München and FFB, Munich, Germany (S.H.H.); and Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
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Posso M, Puig T, Carles M, Rué M, Canelo-Aybar C, Bonfill X. Effectiveness and cost-effectiveness of double reading in digital mammography screening: A systematic review and meta-analysis. Eur J Radiol 2017; 96:40-49. [PMID: 29103474 DOI: 10.1016/j.ejrad.2017.09.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Revised: 09/14/2017] [Accepted: 09/19/2017] [Indexed: 10/18/2022]
Abstract
PURPOSE Double reading is the strategy of choice for mammogram interpretation in screening programmes. It remains, however, unknown whether double reading is still the strategy of choice in the context of digital mammography. Our aim was to determine the effectiveness and cost-effectiveness of double reading versus single reading of digital mammograms in screening programmes. METHODS We performed a systematic review by searching the PubMed, Embase, and Cochrane Library databases up to April 2017. We used the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool and CHEERS (Consolidated Health Economic Evaluation Reporting Standards) checklist to assess the methodological quality of the diagnostic studies and economic evaluations, respectively. A proportion's meta-analysis approach, 95% Confidence Intervals (95% CI) and test of heterogeneity (P values) were used for pooled results. Costs are expressed US$ PPP (United States Dollar purchasing power parities). The PROSPERO ID of this Systematic Review's protocol is CRD42014013804. RESULTS Of 1473 potentially relevant hits, four high-quality studies were included. The pooled cancer detection rate of double reading was 6.01 per 1000 screens (CI: 4.47‰-7.77‰), and it was 5.65 per 1000 screens (CI: 3.95‰-7.65‰) for single reading (P=0.76). The pooled proportion of false-positives of double reading was 47.03 per 1000 screens (CI: 39.13‰-55.62‰) and it was 40.60 per 1000 screens (CI: 38.58‰-42.67‰) for single reading (P=0.12). One study reported, for double reading, an ICER (Incremental Cost-Effectiveness Ratio) of 16,684 Euros (24,717 US$ PPP; 2015 value) per detected cancer. Single reading+CAD (computer-aided-detection) was cost-effective in Japan. CONCLUSION The evidence of benefit for double reading compared to single reading for digital mammography interpretation is scarce. Double reading seems to increase operational costs, have a not significantly higher false-positive rate, and a similar cancer detection rate.
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Affiliation(s)
- Margarita Posso
- Department of Clinical Epidemiology and Public Health, Hospital de la Santa Creu i Sant Pau (IIB Sant Pau), Barcelona, Spain; Iberoamerican Cochrane Centre, Barcelona, Spain.
| | - Teresa Puig
- Department of Clinical Epidemiology and Public Health, Hospital de la Santa Creu i Sant Pau (IIB Sant Pau), Barcelona, Spain; Universitat Autònoma de Barcelona (UAB), Barcelona, Spain.
| | | | - Montserrat Rué
- Basic Medical Sciences Department, Biomedical Research Institut of Lleida (IRBLLEIDA), Universitat de Lleida, Lleida, Spain.
| | - Carlos Canelo-Aybar
- Iberoamerican Cochrane Centre, Barcelona, Spain; School of Medicine, Peruvian University of Applied Sciences, Lima, Peru.
| | - Xavier Bonfill
- Department of Clinical Epidemiology and Public Health, Hospital de la Santa Creu i Sant Pau (IIB Sant Pau), Barcelona, Spain; Universitat Autònoma de Barcelona (UAB), Barcelona, Spain; Iberoamerican Cochrane Centre, Barcelona, Spain; CIBER of Epidemiology and Public Health (CIBERESP), Spain.
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Mordang JJ, Gubern-Mérida A, den Heeten G, Karssemeijer N. Reducing false positives of microcalcification detection systems by removal of breast arterial calcifications. Med Phys 2016; 43:1676. [PMID: 27036566 DOI: 10.1118/1.4943376] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE In the past decades, computer-aided detection (CADe) systems have been developed to aid screening radiologists in the detection of malignant microcalcifications. These systems are useful to avoid perceptual oversights and can increase the radiologists' detection rate. However, due to the high number of false positives marked by these CADe systems, they are not yet suitable as an independent reader. Breast arterial calcifications (BACs) are one of the most frequent false positives marked by CADe systems. In this study, a method is proposed for the elimination of BACs as positive findings. Removal of these false positives will increase the performance of the CADe system in finding malignant microcalcifications. METHODS A multistage method is proposed for the removal of BAC findings. The first stage consists of a microcalcification candidate selection, segmentation and grouping of the microcalcifications, and classification to remove obvious false positives. In the second stage, a case-based selection is applied where cases are selected which contain BACs. In the final stage, BACs are removed from the selected cases. The BACs removal stage consists of a GentleBoost classifier trained on microcalcification features describing their shape, topology, and texture. Additionally, novel features are introduced to discriminate BACs from other positive findings. RESULTS The CADe system was evaluated with and without BACs removal. Here, both systems were applied on a validation set containing 1088 cases of which 95 cases contained malignant microcalcifications. After bootstrapping, free-response receiver operating characteristics and receiver operating characteristics analyses were carried out. Performance between the two systems was compared at 0.98 and 0.95 specificity. At a specificity of 0.98, the sensitivity increased from 37% to 52% and the sensitivity increased from 62% up to 76% at a specificity of 0.95. Partial areas under the curve in the specificity range of 0.8-1.0 were significantly different between the system without BACs removal and the system with BACs removal, 0.129 ± 0.009 versus 0.144 ± 0.008 (p<0.05), respectively. Additionally, the sensitivity at one false positive per 50 cases and one false positive per 25 cases increased as well, 37% versus 51% (p<0.05) and 58% versus 67% (p<0.05) sensitivity, respectively. Additionally, the CADe system with BACs removal reduces the number of false positives per case by 29% on average. The same sensitivity at one false positive per 50 cases in the CADe system without BACs removal can be achieved at one false positive per 80 cases in the CADe system with BACs removal. CONCLUSIONS By using dedicated algorithms to detect and remove breast arterial calcifications, the performance of CADe systems can be improved, in particular, at false positive rates representative for operating points used in screening.
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Affiliation(s)
- Jan-Jurre Mordang
- Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
| | - Albert Gubern-Mérida
- Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
| | - Gerard den Heeten
- The National Training Centre for Breast Cancer Screening, Nijmegen 6503 GJ, The Netherlands and Department of Radiology, Amsterdam Medical Center, Amsterdam 1100 DD, The Netherlands
| | - Nico Karssemeijer
- Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
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Posso M, Carles M, Rué M, Puig T, Bonfill X. Cost-Effectiveness of Double Reading versus Single Reading of Mammograms in a Breast Cancer Screening Programme. PLoS One 2016; 11:e0159806. [PMID: 27459663 PMCID: PMC4961365 DOI: 10.1371/journal.pone.0159806] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Accepted: 07/10/2016] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVES The usual practice in breast cancer screening programmes for mammogram interpretation is to perform double reading. However, little is known about its cost-effectiveness in the context of digital mammography. Our purpose was to evaluate the cost-effectiveness of double reading versus single reading of digital mammograms in a population-based breast cancer screening programme. METHODS Data from 28,636 screened women was used to establish a decision-tree model and to compare three strategies: 1) double reading; 2) double reading for women in their first participation and single reading for women in their subsequent participations; and 3) single reading. We calculated the incremental cost-effectiveness ratio (ICER), which was defined as the expected cost per one additionally detected cancer. We performed a deterministic sensitivity analysis to test the robustness of the ICER. RESULTS The detection rate of double reading (5.17‰) was similar to that of single reading (4.78‰; P = .768). The mean cost of each detected cancer was €8,912 for double reading and €8,287 for single reading. The ICER of double reading versus single reading was €16,684. The sensitivity analysis showed variations in the ICER according to the sensitivity of reading strategies. The strategy that combines double reading in first participation with single reading in subsequent participations was ruled out due to extended dominance. CONCLUSIONS From our results, double reading appears not to be a cost-effective strategy in the context of digital mammography. Double reading would eventually be challenged in screening programmes, as single reading might entail important net savings without significantly changing the cancer detection rate. These results are not conclusive and should be confirmed in prospective studies that investigate long-term outcomes like quality adjusted life years (QALYs).
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Affiliation(s)
- Margarita Posso
- Service of Clinical Epidemiology and Public Health, Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona, Spain
| | | | - Montserrat Rué
- Basic Medical Sciences Department, Biomedical Research Institut of Lleida (IRBLLEIDA), Universitat de Lleida, Lleida, Spain
| | - Teresa Puig
- Service of Clinical Epidemiology and Public Health, Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona, Spain
- Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Xavier Bonfill
- Service of Clinical Epidemiology and Public Health, Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona, Spain
- Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
- CIBER of Epidemiology and Public Health (CIBERESP), Barcelona, Spain
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Weber RJP, van Bommel RMG, Setz-Pels W, Voogd AC, Klompenhouwer EG, Louwman MW, Strobbe LJA, Tjan-Heijnen VCG, Duijm LEM. Type and Extent of Surgery for Screen-Detected and Interval Cancers at Blinded Versus Nonblinded Double-Reading in a Population-Based Screening Mammography Program. Ann Surg Oncol 2016; 23:3822-3830. [DOI: 10.1245/s10434-016-5295-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Indexed: 01/02/2023]
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