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Zhang M, Wang Q, Zhang G, Li G, Jin R, Xing H. A nomogram prognostic model for early hepatocellular carcinoma with diabetes mellitus after primary liver resection based on the admission characteristics. Front Pharmacol 2024; 15:1360478. [PMID: 38434702 PMCID: PMC10905961 DOI: 10.3389/fphar.2024.1360478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 02/05/2024] [Indexed: 03/05/2024] Open
Abstract
Background: Patients diagnosed with early-stage hepatocellular carcinoma (HCC) and diabetes mellitus (DM) are at a higher risk of experiencing complications and facing increased mortality rates. Hence, it is crucial to develop personalized clinical strategies for this particular subgroup upon their admission. The objective of this study is to determine the key prognostic factors in early HCC patients who received liver resection combined with DM and develop a practical personalized model for precise prediction of overall survival in these individuals. Method: A total of 1496 patients diagnosed hepatitis B virus (HBV) - related liver cancer from Beijing You'an Hospital were retrospectively enrolled, spanning from 1 January 2014, to 31 December 2019, and ultimately, 622 eligible patients of hepatocellular carcinoma (HCC) patients with diabetes were included in this present investigation. A multivariate COX regression analysis was conducted to identify prognostic factors that are independent of each other and develop a nomogram. The performance of the nomogram was evaluated using various statistical measures such as the C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) in both the training and validation groups. Survival rates were estimated using the Kaplan-Meier method. Results: The study included a total of 622 early HCC patients who underwent liver resection combined with DM. Random Forrest model and Multivariate Cox regression analysis revealed that drinking, tumor number, monocyte-to-lymphocyte ratio, white blood cell count and international normalized ratio at admission were identified as independent prognostic factors for early HCC patients who underwent liver resection combined with DM. The nomogram demonstrated good predictive performance in the training and validation cohorts based on the C-index values of 0 .756 and 0 .739 respectively, as well as the area under the curve values for 3-, 5-, and 8-year overall survival (0.797, 0.807, 0.840, and 0.725, 0.791, 0.855). Calibration curves and decision curve analysis indicated high accuracy and net clinical benefit rates. Furthermore, the nomogram successfully stratified enrolled patients into low-risk and high-risk groups based on their risk of overall survival. The difference in overall survival between these two groups was statistically significant in both the training and validation cohorts (p < 0.0001 and p = 0.0064). Conclusion: Our results indicate that the admission characteristics demonstrate a highly effective ability to predict the overall survival of early HCC patients who have undergone liver resection in combination with DM. The developed model has the potential to support healthcare professionals in making more informed initial clinical judgments for this particular subgroup of patients.
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Affiliation(s)
- Menghan Zhang
- Center of Liver Diseases Division 3, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Qi Wang
- Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Gongming Zhang
- Department of General Surgery Center, Beijing YouAn Hospital, Beijing Institute of Hepatology, Capital Medical University, Beijing, China
| | - Guangming Li
- Department of General Surgery Center, Beijing YouAn Hospital, Beijing Institute of Hepatology, Capital Medical University, Beijing, China
| | - Ronghua Jin
- Beijing Ditan Hospital, Capital Medical University, Beijing, China
- Changping Laboratory, Beijing, China
| | - Huichun Xing
- Center of Liver Diseases Division 3, Beijing Ditan Hospital, Capital Medical University, Beijing, China
- Center of Liver Diseases Division 3, Beijing Ditan Hospital, Peking University, Beijing, China
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Geertse TD, van der Waal D, Vreuls W, Tetteroo E, Duijm LEM, Pijnappel RM, Broeders MJM. The dilemma of recalling well-circumscribed masses in a screening population: A narrative literature review and exploration of Dutch screening practice. Breast 2023:S0960-9776(23)00451-4. [PMID: 37169601 DOI: 10.1016/j.breast.2023.05.001] [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: 02/24/2023] [Revised: 05/01/2023] [Accepted: 05/02/2023] [Indexed: 05/13/2023] Open
Abstract
BACKGROUND In Dutch breast cancer screening, solitary, new or growing well-circumscribed masses should be recalled for further assessment. This results in cancers detected but also in false positive recalls, especially at initial screening. The aim of this study was to determine characteristics of well-circumscribed masses at mammography and identify potential methods to improve the recall strategy. METHODS A systematic literature search was performed using PubMed. In addition, follow-up data were retrieved on all 8860 recalled women in a Dutch screening region from 2014 to 2019. RESULTS Based on 15 articles identified in the literature search, we found that probably benign well-circumscribed masses that were kept under surveillance had a positive predictive value (PPV) of 0-2%. New or enlarging solitary well-circumscribed masses had a PPV of 10-12%. In general the detected carcinomas had a favorable prognosis. In our exploration of screening practice, 25% of recalls (2133/8860) were triggered by a well-circumscribed mass. Those recalls had a PPV of 2.0% for initial and 10.6% for subsequent screening. Most detected carcinomas had a favorable prognosis as well. CONCLUSION To recognize malignancies presenting as well-circumscribed masses, identifying solitary, new or growing lesions is key. This information is missing at initial screening since prior examinations are not available, leading to a low PPV. Access to prior clinical examinations may therefore improve this PPV. In addition, given the generally favorable prognosis of screen-detected malignant well-circumscribed masses, one may opt to recall these lesions at subsequent screening, if grown, rather than at initial screening.
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Affiliation(s)
- Tanya D Geertse
- Dutch Expert Centre for Screening (LRCB), Wijchenseweg 101, 6538 SW, Nijmegen, the Netherlands.
| | - Daniëlle van der Waal
- Dutch Expert Centre for Screening (LRCB), Wijchenseweg 101, 6538 SW, Nijmegen, the Netherlands
| | - Willem Vreuls
- Canisius Wilhelmina Hospital, Department of Radiology Weg Door, Jonkerbos 100, 6532 SZ, Nijmegen, the Netherlands
| | - Eric Tetteroo
- Amphia Hospital, Department of Radiology Molengracht 21, 4818 CK, Breda, the Netherlands
| | - Lucien E M Duijm
- Canisius Wilhelmina Hospital, Department of Radiology Weg Door, Jonkerbos 100, 6532 SZ, Nijmegen, the Netherlands
| | - Ruud M Pijnappel
- Dutch Expert Centre for Screening (LRCB), Wijchenseweg 101, 6538 SW, Nijmegen, the Netherlands; University Medical Centre Utrecht, Utrecht UniversityDepartment of Radiology, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands
| | - Mireille J M Broeders
- Dutch Expert Centre for Screening (LRCB), Wijchenseweg 101, 6538 SW, Nijmegen, the Netherlands; Radboud University Medical CenterDepartment for Health Evidence Geert Grooteplein 21, 6525 EZ, Nijmegen, the Netherlands
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Wang X, Lu J, Song Z, Zhou Y, Liu T, Zhang D. From past to future: Bibliometric analysis of global research productivity on nomogram (2000-2021). Front Public Health 2022; 10:997713. [PMID: 36203677 PMCID: PMC9530946 DOI: 10.3389/fpubh.2022.997713] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 09/02/2022] [Indexed: 01/26/2023] Open
Abstract
Background Nomogram, a visual clinical predictive model, provides a scientific basis for clinical decision making. Herein, we investigated 20 years of nomogram research responses, focusing on current and future trends and analytical challenges. Methods We mined data of scientific literature from the Core Collection of Web of Science, searching for the original articles with title "Nomogram*/Parton Table*/Parton Nomogram*", published within January 1st, 2000 to December 30th, 2021. Data records were validated using HistCite Version and analyzed with a transformable statistical method, the Bibliometrix 3.0 package of R Studio. Results In total, 4,176 original articles written by 19,158 authors were included from 915 sources. Annually, Nomogram publications are continually produced, which have rapidly grown since 2018. China published the most articles; however, its total citations ranked second after the United States. Both total citations and average article citations in the United States rank first globally, and a high degree of cooperation exists between countries. Frontiers in Oncology published the most papers (238); this number has grown rapidly since 2019. Journal of Urology had the highest H-index, with an average increase in publications over the past 20 years. Most research topics were tumor-related, among which tumor risk prediction and prognostic evaluation were the main contents. Research on prognostic assessment is more published and advanced, while risk prediction and diagnosis have good developmental prospects. Furthermore, nomogram of the urinary system has been highly developed. Following advancements in nomogram modeling, it has recently been applied to non-oncological subjects. Conclusion This bibliometric analysis provides a comprehensive overview of the current nomogram status, which could enable better understanding of its development over the years, and provide global researchers a comprehensive analysis and structured information to help identify hot spots and gaps in future research.
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Affiliation(s)
- Xiaoxue Wang
- Department of Health Management, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jingliang Lu
- Lanzhou Information Center, Chinese Academy of Sciences, Lanzhou, China
| | - Zixuan Song
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yangzi Zhou
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Tong Liu
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China,Tong Liu
| | - Dandan Zhang
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China,*Correspondence: Dandan Zhang
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Nomogram for prediction of long-term survival with hepatocellular carcinoma based on NK cell counts. Ann Hepatol 2022; 27:100672. [PMID: 35065261 DOI: 10.1016/j.aohep.2022.100672] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 01/13/2022] [Indexed: 02/04/2023]
Abstract
INTRODUCTION Among all immune cells, natural killer (NK) cells play an important role as the first line of defense against tumor. The purpose of our study is to observe whether the NK cell counts can predict the overall survival of patients with hepatocellular carcinoma (HCC). METHODS To develop a novel model, from January 2010 to June 2015, HCC patients enrolled in Beijing Ditan hospital were divided into training and validation cohort. Cox multiple regression analysis was used to analyze the independent risk factors for 1-year, 3-year and 5-year overall survival (OS) of patients with HCC, and the nomogram was used to establish the prediction model. In addition, the decision tree was established to verify the contribution of NK cell counts to the survival of patients with HCC. RESULTS The model used in predicting overall survival of HCC included six variables (namely, NK cell counts, albumin (ALB) level, alpha-fetoprotein (AFP) level, portal vein tumor thrombus (PVTT), tumor number and treatment). The C-index of nomogram model in HCC patients predicting 1-year, 3-year and 5-year overall survival was 0.858, 0.788 and 0.782 respectively, which was higher than tumor-lymph node-metastasis (TNM) staging system, Okuda, model for end-stage liver disease (MELD), MELD-Na, the Chinese University Prognostic Index (CUPI) and Japan Integrated Staging (JIS) scores (p < 0.001). The decision tree showed the specific 5-year OS probability of HCC patients under different risk factors, and found that NK cell counts were the third in the column contribution. CONCLUSIONS Our study emphasizes the utility of NK cell counts for exploring interactions between long-term survival of HCC patients and predictor variables.
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Xie Y, Zhu Y, Chai W, Zong S, Xu S, Zhan W, Zhang X. Downgrade BI-RADS 4A Patients Using Nomogram Based on Breast Magnetic Resonance Imaging, Ultrasound, and Mammography. Front Oncol 2022; 12:807402. [PMID: 35155244 PMCID: PMC8828585 DOI: 10.3389/fonc.2022.807402] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 01/03/2022] [Indexed: 01/15/2023] Open
Abstract
Objectives To downgrade BI-RADS 4A patients by constructing a nomogram using R software. Materials and Methods A total of 1,717 patients were retrospectively analyzed who underwent preoperative ultrasound, mammography, and magnetic resonance examinations in our hospital from August 2019 to September 2020, and a total of 458 patients of category BI-RADS 4A (mean age, 47 years; range 18–84 years; all women) were included. Multivariable logistic regression was used to screen out the independent influencing parameters that affect the benign and malignant tumors, and the nomogram was constructed by R language to downgrade BI-RADS 4A patients to eligible category. Results Of 458 BI-RADS 4A patients, 273 (59.6%) were degraded to category 3. The malignancy rate of these 273 lesions is 1.5% (4/273) (<2%), and the sensitivity reduced to 99.6%, the specificity increased from 4.41% to 45.3%, and the accuracy increased from 63.4% to 78.8%. Conclusion By constructing a nomogram, some patients can be downgraded to avoid unnecessary biopsy.
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Affiliation(s)
- Yamie Xie
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,College of Medicine, Kunming University of Science and Technology, Department of Ultrasound, The First People's Hospital of Yunnan Province, Kunming, China
| | - Ying Zhu
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weimin Chai
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shaoyun Zong
- College of Medicine, Kunming University of Science and Technology, Department of Ultrasound, The First People's Hospital of Yunnan Province, Kunming, China
| | - Shangyan Xu
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weiwei Zhan
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoxiao Zhang
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Wang B, Chen J. Establishment and validation of a predictive model for mortality within 30 days in patients with sepsis-induced blood pressure drop: A retrospective analysis. PLoS One 2021; 16:e0252009. [PMID: 34015023 PMCID: PMC8136670 DOI: 10.1371/journal.pone.0252009] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 05/09/2021] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVES To establish and validate an individualized nomogram to predict the probability of death within 30 days in patients with sepsis-induced blood pressure drop would help clinical physicians to pay attention to those with higher risk of death after admission to wards. METHODS A total of 1023 patients who were admitted to the Dongyang People's Hospital, China, enrolled in this study. They were divided into model group (717 patients) and validation group (306 patients). The study included 13 variables. The independent risk factors leading to death within 30 days were screened by univariate analyses and multivariate logistic regression analyses and used for Nomogram. The discrimination and correction of the prediction model were assessed by the area under the Receiver Operating Characteristic (ROC) curve and the calibration chart. The clinical effectiveness of the prediction model was assessed by the Decision Curve Analysis (DCA). RESULTS Seven variables were independent risk factors, included peritonitis, respiratory failure, cardiac insufficiency, consciousness disturbance, tumor history, albumin level, and creatinine level at the time of admission. The area under the ROC curve of the model group and validation group was 0.834 and 0.836. The P value of the two sets of calibration charts was 0.702 and 0.866. The DCA curves of the model group and validation group were above the two extreme (insignificant) curves. CONCLUSIONS The model described in this study could effectively predict the death of patients with sepsis-induced blood pressure drop.
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Affiliation(s)
- Bin Wang
- Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Jinhua, Zhejiang Province, China
- * E-mail:
| | - Jianping Chen
- Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Jinhua, Zhejiang Province, China
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Shen L, Ma X, Jiang T, Shen X, Yang W, You C, Peng W. Malignancy Risk Stratification Prediction of Amorphous Calcifications Based on Clinical and Mammographic Features. Cancer Manag Res 2021; 13:235-245. [PMID: 33469367 PMCID: PMC7811441 DOI: 10.2147/cmar.s286269] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 12/17/2020] [Indexed: 12/16/2022] Open
Abstract
Purpose To explore the potential factors influencing the malignancy risk of amorphous calcifications and establish a predictive nomogram for malignancy risk stratification. Patients and Methods Consecutive mammograms from January 2013 to December 2018 were retrospectively reviewed. Traditional clinical features were recorded, and mammographic features were estimated according to the 5th BI-RADS. Included calcifications were randomly divided into the training and validation cohorts. A nomogram was developed to graphically predict the risk of malignancy (risk) based on stepwise multivariate logistic regression analysis. The discrimination and calibration performance of the model were assessed in both the training and validation cohorts. Results Finally, 1018 amorphous calcifications with final pathological results in 907 women were identified with a malignancy rate of 28.4% (95% CI: 25.7%, 31.3%). The malignancy rates of subgroups divided by the distribution of calcifications, quantity of calcifications, age, menopausal status and family history of cancer were significantly different. There were 712 cases and 306 cases in the training and validation cohorts. The prediction nomogram was finally developed based on four risk factors, including age and distribution, maximum diameter and quantity of calcifications. The AUC of the nomogram was 0.799 (95% CI: 0.761, 0.836) in the training cohort and 0.795 (95% CI: 0.738, 0.852) in the validation cohort. Conclusion On mammography, the distribution, maximum diameter and quantity of calcifications are independent predictors of malignant amorphous calcifications and can be easily obtained in the clinic. The nomogram developed in this study for individualized malignancy risk stratification of amorphous calcifications shows good discrimination performance.
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Affiliation(s)
- Lijuan Shen
- Shanghai Institute of Medical Imaging, Shanghai, People's Republic of China.,Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Xiaowen Ma
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Tingting Jiang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Xigang Shen
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Wentao Yang
- Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
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Wang H, Lai J, Li J, Gu R, Liu F, Hu Y, Mei J, Jiang X, Shen S, Yu F, Su F. Does establishing a preoperative nomogram including ultrasonographic findings help predict the likelihood of malignancy in patients with microcalcifications? Cancer Imaging 2019; 19:46. [PMID: 31269987 PMCID: PMC6610836 DOI: 10.1186/s40644-019-0229-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2018] [Accepted: 06/17/2019] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Mammography (MG) is highly sensitive for detecting microcalcifications, but has low specificity. This study investigates whether establishing a preoperative nomogram including ultrasonographic findings can help predict the likelihood of malignancy in patients with mammographic microcalcification. METHODS Between May 2012 and January 2017, 475 patients with suspicious microcalcifications detected on MG underwent ultrasonography (US). The χ2 test was used to screen risk factors among the variables. Then, a multivariate logistic regression analysis was performed to identify independent predictors of malignant microcalcifications. A mammographic nomogram (M nomogram) and mammographic-ultrasonographic nomogram (M-U nomogram) were established based on multivariate logistic regression models. The discriminatory ability and clinical utility of both nomograms were compared by the receiver operating characteristics curve and decision curve analysis. The calibration ability was evaluated using a calibration curve. RESULTS Among the cases, 68.2% (324/475) were pathologically diagnosed as breast cancer and 31.8% (151/475) were benign lesions. Based on multivariate logistic regression analysis, age, clinical manifestation, morphology and distribution of microcalcifications on MG and lesions associated with microcalcifications on US were confirmed as independent predictors of malignant microcalcifications. In terms of discrimination ability, the C-index of the M-U nomogram was significantly higher than that of the M nomogram (0.917 vs 0.897, p = 0.006). The bias-corrected curve was close to the ideal line in the calibration curve. Decision curve analysis suggested that the M-U nomogram was superior to M nomogram. CONCLUSIONS Combining mammographic parameters with ultrasonographic findings in a nomogram provided better performance than an M nomogram alone, especially for dense breasts, which suggests the value of ultrasonographic finding for individualized prediction of malignancy in patients with microcalcifications.
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Affiliation(s)
- Hongli Wang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yanjiang Road. west No.107, YueXiu district, Guangzhou, Guangdong, 510120, People's Republic of China
| | - Jianguo Lai
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yanjiang Road. west No.107, YueXiu district, Guangzhou, Guangdong, 510120, People's Republic of China
| | - Jiao Li
- Department of Radiology, Sun Yat-sen University Cancer Center, Dongfeng East Road No.651, Yuexiu District, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Ran Gu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yanjiang Road. west No.107, YueXiu district, Guangzhou, Guangdong, 510120, People's Republic of China.,Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yingfeng Road No. 33, HaiZhu district, Guangzhou, Guangdong, 510288, People's Republic of China
| | - Fengtao Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yanjiang Road. west No.107, YueXiu district, Guangzhou, Guangdong, 510120, People's Republic of China.,Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yingfeng Road No. 33, HaiZhu district, Guangzhou, Guangdong, 510288, People's Republic of China
| | - Yue Hu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yanjiang Road. west No.107, YueXiu district, Guangzhou, Guangdong, 510120, People's Republic of China.,Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yingfeng Road No. 33, HaiZhu district, Guangzhou, Guangdong, 510288, People's Republic of China
| | - Jingsi Mei
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yanjiang Road. west No.107, YueXiu district, Guangzhou, Guangdong, 510120, People's Republic of China.,Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yingfeng Road No. 33, HaiZhu district, Guangzhou, Guangdong, 510288, People's Republic of China
| | - Xiaofang Jiang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yanjiang Road. west No.107, YueXiu district, Guangzhou, Guangdong, 510120, People's Republic of China.,Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yingfeng Road No. 33, HaiZhu district, Guangzhou, Guangdong, 510288, People's Republic of China
| | - Shiyu Shen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yanjiang Road. west No.107, YueXiu district, Guangzhou, Guangdong, 510120, People's Republic of China.,Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yingfeng Road No. 33, HaiZhu district, Guangzhou, Guangdong, 510288, People's Republic of China
| | - Fengyan Yu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yanjiang Road. west No.107, YueXiu district, Guangzhou, Guangdong, 510120, People's Republic of China. .,Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yingfeng Road No. 33, HaiZhu district, Guangzhou, Guangdong, 510288, People's Republic of China.
| | - Fengxi Su
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yanjiang Road. west No.107, YueXiu district, Guangzhou, Guangdong, 510120, People's Republic of China. .,Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yingfeng Road No. 33, HaiZhu district, Guangzhou, Guangdong, 510288, People's Republic of China.
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Yang J, Wang T, Yang L, Wang Y, Li H, Zhou X, Zhao W, Ren J, Li X, Tian J, Huang L. Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using Mammography-Based Radiomics Method. Sci Rep 2019; 9:4429. [PMID: 30872652 PMCID: PMC6418289 DOI: 10.1038/s41598-019-40831-z] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 02/14/2019] [Indexed: 12/13/2022] Open
Abstract
It is difficult to accurately assess axillary lymph nodes metastasis and the diagnosis of axillary lymph nodes in patients with breast cancer is invasive and has low-sensitivity preoperatively. This study aims to develop a mammography-based radiomics nomogram for the preoperative prediction of ALN metastasis in patients with breast cancer. This study enrolled 147 patients with clinicopathologically confirmed breast cancer and preoperative mammography. Features were extracted from each patient's mammography images. The least absolute shrinkage and selection operator regression method was used to select features and build a signature in the primary cohort. The performance of the signature was assessed using support vector machines. We developed a nomogram by incorporating the signature with the clinicopathologic risk factors. The nomogram performance was estimated by its calibration ability in the primary and validation cohorts. The signature was consisted of 10 selected ALN-status-related features. The AUC of the signature from the primary cohort was 0.895 (95% CI, 0.887-0.909) and 0.875 (95% CI, 0.698-0.891) for the validation cohort. The C-Index of the nomogram from the primary cohort was 0.779 (95% CI, 0.752-0.793) and 0.809 (95% CI, 0.794-0.833) for the validation cohort. Our nomogram is a reliable and non-invasive tool for preoperative prediction of ALN status and can be used to optimize current treatment strategy for breast cancer patients.
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Affiliation(s)
- Jingbo Yang
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China
| | - Tao Wang
- Department of Radiology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, 710068, China
| | - Lifeng Yang
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China
| | - Yubo Wang
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China
| | - Hongmei Li
- Department of Breast Diseases, Yan'an University Affiliated Hospital, Yan'an, Shaanxi, 716000, China
| | - Xiaobo Zhou
- Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina, 27157, USA.
| | - Weiling Zhao
- Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina, 27157, USA
| | - Junchan Ren
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China
| | - Xiaoyong Li
- Department of Breast Diseases, Yan'an University Affiliated Hospital, Yan'an, Shaanxi, 716000, China
| | - Jie Tian
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China
| | - Liyu Huang
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China.
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Al-Ajmi K, Lophatananon A, Yuille M, Ollier W, Muir KR. Review of non-clinical risk models to aid prevention of breast cancer. Cancer Causes Control 2018; 29:967-986. [PMID: 30178398 PMCID: PMC6182451 DOI: 10.1007/s10552-018-1072-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 08/10/2018] [Indexed: 12/29/2022]
Abstract
A disease risk model is a statistical method which assesses the probability that an individual will develop one or more diseases within a stated period of time. Such models take into account the presence or absence of specific epidemiological risk factors associated with the disease and thereby potentially identify individuals at higher risk. Such models are currently used clinically to identify people at higher risk, including identifying women who are at increased risk of developing breast cancer. Many genetic and non-genetic breast cancer risk models have been developed previously. We have evaluated existing non-genetic/non-clinical models for breast cancer that incorporate modifiable risk factors. This review focuses on risk models that can be used by women themselves in the community in the absence of clinical risk factors characterization. The inclusion of modifiable factors in these models means that they can be used to improve primary prevention and health education pertinent for breast cancer. Literature searches were conducted using PubMed, ScienceDirect and the Cochrane Database of Systematic Reviews. Fourteen studies were eligible for review with sample sizes ranging from 654 to 248,407 participants. All models reviewed had acceptable calibration measures, with expected/observed (E/O) ratios ranging from 0.79 to 1.17. However, discrimination measures were variable across studies with concordance statistics (C-statistics) ranging from 0.56 to 0.89. We conclude that breast cancer risk models that include modifiable risk factors have been well calibrated but have less ability to discriminate. The latter may be a consequence of the omission of some significant risk factors in the models or from applying models to studies with limited sample sizes. More importantly, external validation is missing for most of the models. Generalization across models is also problematic as some variables may not be considered applicable to some populations and each model performance is conditioned by particular population characteristics. In conclusion, it is clear that there is still a need to develop a more reliable model for estimating breast cancer risk which has a good calibration, ability to accurately discriminate high risk and with better generalizability across populations.
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Affiliation(s)
- Kawthar Al-Ajmi
- Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, The University of Manchester, Manchester, M139 PL UK
| | - Artitaya Lophatananon
- Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, The University of Manchester, Manchester, M139 PL UK
| | - Martin Yuille
- Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, The University of Manchester, Manchester, M139 PL UK
| | - William Ollier
- Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, The University of Manchester, Manchester, M139 PL UK
| | - Kenneth R. Muir
- Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, The University of Manchester, Manchester, M139 PL UK
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Wu M, Ma J. Association Between Imaging Characteristics and Different Molecular Subtypes of Breast Cancer. Acad Radiol 2017; 24:426-434. [PMID: 27955963 DOI: 10.1016/j.acra.2016.11.012] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Revised: 09/18/2016] [Accepted: 11/10/2016] [Indexed: 01/09/2023]
Abstract
RATIONALE AND OBJECTIVE Breast cancer can be divided into four major molecular subtypes based on the expression of hormone receptor (estrogen receptor and progesterone receptor), human epidermal growth factor receptor 2, HER2 status, and molecular proliferation rate (Ki67). In this study, we sought to investigate the association between breast cancer subtype and radiological findings in the Chinese population. MATERIALS AND METHODS Medical records of 300 consecutive invasive breast cancer patients were reviewed from the database: the Breast Imaging Reporting and Data System. The imaging characteristics of the lesions were evaluated. The molecular subtypes of breast cancer were classified into four types: luminal A, luminal B, HER2 overexpressed (HER2), and basal-like breast cancer (BLBC). Univariate and multivariate logistic regression analyses were performed to assess the association between the subtype (dependent variable) and mammography or 15 magnetic resonance imaging (MRI) indicators (independent variables). RESULTS Luminal A and B subtypes were commonly associated with "clustered calcification distribution," "nipple invasion," or "skin invasion" (P <0.05). The BLBC subtype was more commonly associated with "rim enhancement" and persistent inflow type enhancement in delayed phase (P <0.05). HER2 overexpressed cancers showed association with persistent enhancement in the delayed phase on MRI and "clustered calcification distribution" on mammography (P <0.05). CONCLUSION Certain radiological features are strongly associated with the molecular subtype and hormone receptor status of breast tumor, which are potentially useful tools in the diagnosis and subtyping of breast cancer.
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Burnside ES, Liu J, Wu Y, Onitilo AA, McCarty CA, Page CD, Peissig PL, Trentham-Dietz A, Kitchner T, Fan J, Yuan M. Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy. Acad Radiol 2016; 23:62-9. [PMID: 26514439 DOI: 10.1016/j.acra.2015.09.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2015] [Revised: 09/15/2015] [Accepted: 09/28/2015] [Indexed: 01/10/2023]
Abstract
RATIONALE AND OBJECTIVES The discovery of germline genetic variants associated with breast cancer has engendered interest in risk stratification for improved, targeted detection and diagnosis. However, there has yet to be a comparison of the predictive ability of these genetic variants with mammography abnormality descriptors. MATERIALS AND METHODS Our institutional review board-approved, Health Insurance Portability and Accountability Act-compliant study utilized a personalized medicine registry in which participants consented to provide a DNA sample and to participate in longitudinal follow-up. In our retrospective, age-matched, case-controlled study of 373 cases and 395 controls who underwent breast biopsy, we collected risk factors selected a priori based on the literature, including demographic variables based on the Gail model, common germline genetic variants, and diagnostic mammography findings according to Breast Imaging Reporting and Data System (BI-RADS). We developed predictive models using logistic regression to determine the predictive ability of (1) demographic variables, (2) 10 selected genetic variants, or (3) mammography BI-RADS features. We evaluated each model in turn by calculating a risk score for each patient using 10-fold cross-validation, used this risk estimate to construct Receiver Operator Characteristic Curve (ROC) curves, and compared the area under the ROC curve (AUC) of each using the DeLong method. RESULTS The performance of the regression model using demographic risk factors was not statistically different from the model using genetic variants (P = 0.9). The model using mammography features (AUC = 0.689) was superior to both the demographic model (AUC = .598; P < 0.001) and the genetic model (AUC = .601; P < 0.001). CONCLUSIONS BI-RADS features exceeded the ability of demographic and 10 selected germline genetic variants to predict breast cancer in women recommended for biopsy.
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Klompenhouwer E, Weber R, Voogd A, den Heeten G, Strobbe L, Broeders M, Tjan-Heijnen V, Duijm L. Arbitration of discrepant BI-RADS 0 recalls by a third reader at screening mammography lowers recall rate but not the cancer detection rate and sensitivity at blinded and non-blinded double reading. Breast 2015; 24:601-7. [DOI: 10.1016/j.breast.2015.06.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2015] [Revised: 05/31/2015] [Accepted: 06/06/2015] [Indexed: 11/28/2022] Open
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Benndorf M, Burnside ES, Herda C, Langer M, Kotter E. External validation of a publicly available computer assisted diagnostic tool for mammographic mass lesions with two high prevalence research datasets. Med Phys 2015; 42:4987-96. [PMID: 26233224 DOI: 10.1118/1.4927260] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Lesions detected at mammography are described with a highly standardized terminology: the breast imaging-reporting and data system (BI-RADS) lexicon. Up to now, no validated semantic computer assisted classification algorithm exists to interactively link combinations of morphological descriptors from the lexicon to a probabilistic risk estimate of malignancy. The authors therefore aim at the external validation of the mammographic mass diagnosis (MMassDx) algorithm. A classification algorithm like MMassDx must perform well in a variety of clinical circumstances and in datasets that were not used to generate the algorithm in order to ultimately become accepted in clinical routine. METHODS The MMassDx algorithm uses a naïve Bayes network and calculates post-test probabilities of malignancy based on two distinct sets of variables, (a) BI-RADS descriptors and age ("descriptor model") and (b) BI-RADS descriptors, age, and BI-RADS assessment categories ("inclusive model"). The authors evaluate both the MMassDx (descriptor) and MMassDx (inclusive) models using two large publicly available datasets of mammographic mass lesions: the digital database for screening mammography (DDSM) dataset, which contains two subsets from the same examinations-a medio-lateral oblique (MLO) view and cranio-caudal (CC) view dataset-and the mammographic mass (MM) dataset. The DDSM contains 1220 mass lesions and the MM dataset contains 961 mass lesions. The authors evaluate discriminative performance using area under the receiver-operating-characteristic curve (AUC) and compare this to the BI-RADS assessment categories alone (i.e., the clinical performance) using the DeLong method. The authors also evaluate whether assigned probabilistic risk estimates reflect the lesions' true risk of malignancy using calibration curves. RESULTS The authors demonstrate that the MMassDx algorithms show good discriminatory performance. AUC for the MMassDx (descriptor) model in the DDSM data is 0.876/0.895 (MLO/CC view) and AUC for the MMassDx (inclusive) model in the DDSM data is 0.891/0.900 (MLO/CC view). AUC for the MMassDx (descriptor) model in the MM data is 0.862 and AUC for the MMassDx (inclusive) model in the MM data is 0.900. In all scenarios, MMassDx performs significantly better than clinical performance, P < 0.05 each. The authors furthermore demonstrate that the MMassDx algorithm systematically underestimates the risk of malignancy in the DDSM and MM datasets, especially when low probabilities of malignancy are assigned. CONCLUSIONS The authors' results reveal that the MMassDx algorithms have good discriminatory performance but less accurate calibration when tested on two independent validation datasets. Improvement in calibration and testing in a prospective clinical population will be important steps in the pursuit of translation of these algorithms to the clinic.
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Affiliation(s)
- Matthias Benndorf
- Department of Radiology, University Hospital Freiburg, Hugstetter Straße 55, Freiburg 79106, Germany
| | - Elizabeth S Burnside
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, 600 Highland Avenue, Madison, Wisconsin 53792
| | - Christoph Herda
- Kantonsspital Graubünden, Loesstraße 170, Chur 7000, Switzerland
| | - Mathias Langer
- Department of Radiology, University Hospital Freiburg, Hugstetter Straße 55, Freiburg 79106, Germany
| | - Elmar Kotter
- Department of Radiology, University Hospital Freiburg, Hugstetter Straße 55, Freiburg 79106, Germany
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Benndorf M, Kotter E, Langer M, Herda C, Wu Y, Burnside ES. Development of an online, publicly accessible naive Bayesian decision support tool for mammographic mass lesions based on the American College of Radiology (ACR) BI-RADS lexicon. Eur Radiol 2015; 25:1768-75. [PMID: 25576230 PMCID: PMC4420692 DOI: 10.1007/s00330-014-3570-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2014] [Revised: 12/11/2014] [Accepted: 12/15/2014] [Indexed: 11/26/2022]
Abstract
PURPOSE To develop and validate a decision support tool for mammographic mass lesions based on a standardized descriptor terminology (BI-RADS lexicon) to reduce variability of practice. MATERIALS AND METHODS We used separate training data (1,276 lesions, 138 malignant) and validation data (1,177 lesions, 175 malignant). We created naïve Bayes (NB) classifiers from the training data with tenfold cross-validation. Our "inclusive model" comprised BI-RADS categories, BI-RADS descriptors, and age as predictive variables; our "descriptor model" comprised BI-RADS descriptors and age. The resulting NB classifiers were applied to the validation data. We evaluated and compared classifier performance with ROC-analysis. RESULTS In the training data, the inclusive model yields an AUC of 0.959; the descriptor model yields an AUC of 0.910 (P < 0.001). The inclusive model is superior to the clinical performance (BI-RADS categories alone, P < 0.001); the descriptor model performs similarly. When applied to the validation data, the inclusive model yields an AUC of 0.935; the descriptor model yields an AUC of 0.876 (P < 0.001). Again, the inclusive model is superior to the clinical performance (P < 0.001); the descriptor model performs similarly. CONCLUSION We consider our classifier a step towards a more uniform interpretation of combinations of BI-RADS descriptors. We provide our classifier at www.ebm-radiology.com/nbmm/index.html . KEY POINTS • We provide a decision support tool for mammographic masses at www.ebm-radiology.com/nbmm/index.html . • Our tool may reduce variability of practice in BI-RADS category assignment. • A formal analysis of BI-RADS descriptors may enhance radiologists' diagnostic performance.
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Affiliation(s)
- Matthias Benndorf
- Department of Radiology, University Hospital Freiburg, Hugstetter Straße 55, 79106, Freiburg, Germany,
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Evaluation of low-energy contrast-enhanced spectral mammography images by comparing them to full-field digital mammography using EUREF image quality criteria. Eur Radiol 2015; 25:2813-20. [PMID: 25813015 PMCID: PMC4562003 DOI: 10.1007/s00330-015-3695-2] [Citation(s) in RCA: 92] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2014] [Revised: 02/02/2015] [Accepted: 02/24/2015] [Indexed: 11/26/2022]
Abstract
Objective Contrast-enhanced spectral mammography (CESM) examination results in a low-energy (LE) and contrast-enhanced image. The LE appears similar to a full-field digital mammogram (FFDM). Our aim was to evaluate LE CESM image quality by comparing it to FFDM using criteria defined by the European Reference Organization for Quality Assured Breast Screening and Diagnostic Services (EUREF). Methods A total of 147 cases with both FFDM and LE images were independently scored by two experienced radiologists using these (20) EUREF criteria. Contrast detail measurements were performed using a dedicated phantom. Differences in image quality scores, average glandular dose, and contrast detail measurements between LE and FFDM were tested for statistical significance. Results No significant differences in image quality scores were observed between LE and FFDM images for 17 out of 20 criteria. LE scored significantly lower on one criterion regarding the sharpness of the pectoral muscle (p < 0.001), and significantly better on two criteria on the visualization of micro-calcifications (p = 0.02 and p = 0.034). Dose and contrast detail measurements did not reveal any physical explanation for these observed differences. Conclusions Low-energy CESM images are non-inferior to FFDM images. From this perspective FFDM can be omitted in patients with an indication for CESM. Key Points • Low-energy CESM images are non-inferior to FFDM images. • Micro-calcifications are significantly more visible on LE CESM than on FFDM. • There is no physical explanation for this improved visibility of micro-calcifications. • There is no need for an extra FFDM when CESM is indicated.
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Gao H, Aiello Bowles EJ, Carrell D, Buist DSM. Using natural language processing to extract mammographic findings. J Biomed Inform 2015; 54:77-84. [PMID: 25661260 DOI: 10.1016/j.jbi.2015.01.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2014] [Revised: 01/21/2015] [Accepted: 01/25/2015] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Structured data on mammographic findings are difficult to obtain without manual review. We developed and evaluated a rule-based natural language processing (NLP) system to extract mammographic findings from free-text mammography reports. MATERIALS AND METHODS The NLP system extracted four mammographic findings: mass, calcification, asymmetry, and architectural distortion, using a dictionary look-up method on 93,705 mammography reports from Group Health. Status annotations and anatomical location annotation were associated to each NLP detected finding through association rules. After excluding negated, uncertain, and historical findings, affirmative mentions of detected findings were summarized. Confidence flags were developed to denote reports with highly confident NLP results and reports with possible NLP errors. A random sample of 100 reports was manually abstracted to evaluate the accuracy of the system. RESULTS The NLP system correctly coded 96-99 out of our sample of 100 reports depending on findings. Measures of sensitivity, specificity and negative predictive values exceeded 0.92 for all findings. Positive predictive values were relatively low for some findings due to their low prevalence. DISCUSSION Our NLP system was implemented entirely in SAS Base, which makes it portable and easy to implement. It performed reasonably well with multiple applications, such as using confidence flags as a filter to improve the efficiency of manual review. Refinements of library and association rules, and testing on more diverse samples may further improve its performance. CONCLUSION Our NLP system successfully extracts clinically useful information from mammography reports. Moreover, SAS is a feasible platform for implementing NLP algorithms.
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Affiliation(s)
- Hongyuan Gao
- Group Health Research Institute, Seattle, WA, USA.
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Abstract
PURPOSE OF REVIEW Breast cancer is the most common cancer in women worldwide. This review will focus on current prevention strategies for women at high risk. RECENT FINDINGS The identification of women who are at high risk of developing breast cancer is key to breast cancer prevention. Recent findings have shown that the inclusion of breast density and a panel of low-penetrance genetic polymorphisms can improve risk estimation compared with previous models. Preventive therapy with aromatase inhibitors has produced large reductions in breast cancer incidence in postmenopausal women. Tamoxifen confers long-term protection and is the only proven preventive treatment for premenopausal women. Several other agents, including metformin, bisphosphonates, aspirin and statins, have been found to be effective in nonrandomized settings. SUMMARY There are many options for the prevention of oestrogen-positive breast cancer, in postmenopausal women who can be given a selective oestrogen receptor modulator or an aromatase inhibitor. It still remains unclear how to prevent oestrogen-negative breast cancer, which occurs more often in premenopausal women. Identification of women at high risk of the disease is crucial, and the inclusion of breast density and a panel of genetic polymorphisms, which individually have low penetrance, can improve risk assessment.
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Affiliation(s)
- Ivana Sestak
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
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Grimm LJ, Ghate SV, Yoon SC, Kuzmiak CM, Kim C, Mazurowski MA. Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features. Med Phys 2014; 41:031909. [DOI: 10.1118/1.4866379] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
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