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Cureus Nov 2023Breast cancer is a prevalent global health concern, necessitating accurate diagnostic tools for effective management. Diagnostic imaging plays a pivotal role in breast... (Review)
Review
Breast cancer is a prevalent global health concern, necessitating accurate diagnostic tools for effective management. Diagnostic imaging plays a pivotal role in breast cancer diagnosis, staging, treatment planning, and outcome evaluation. Radiomics is an emerging field of study in medical imaging that contains a broad set of computational methods to extract quantitative features from radiographic images. This can be utilized to guide diagnosis, treatment response, and prognosis in clinical settings. A systematic review was performed in concordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. Quality was assessed using the radiomics quality score. Diagnostic sensitivity and specificity of radiomics analysis, with 95% confidence intervals (CIs), were included for meta-analysis. The area under the curve analysis was recorded. An extensive statistical analysis was performed following the Cochrane guidelines. Statistical significance was determined if p-values were less than 0.05. Statistical analyses were conducted using Review Manager (RevMan), Version 5.4.1. A total of 31 manuscripts involving 8,773 patients were included, with 17 contributing to the meta-analysis. The cohort comprised 56.2% malignant breast cancers and 43.8% benign breast lesions. MRI demonstrated a sensitivity of 0.91 (95% CI: 0.89-0.92) and a specificity of 0.84 (95% CI: 0.82-0.86) in differentiating between benign and malignant breast cancers. Mammography-based radiomic features predicted breast cancer subtype with a sensitivity of 0.79 (95% CI: 0.76-0.82) and a specificity of 0.81 (95% CI: 0.79-0.84). Ultrasound-based analysis yielded a sensitivity of 0.92 (95% CI: 0.90-0.94) and a specificity of 0.85 (95% CI: 0.83-0.88). Only one study reported the results of radiomic evaluation from CT, which had a sensitivity of 0.95 (95% CI: 0.88-0.99) and a specificity of 0.56 (95% CI: 0.45-0.67). Across different imaging modalities, radiomics exhibited robust diagnostic accuracy in differentiating benign and malignant breast lesions. The results underscore the potential of radiomic assessment as a minimally invasive alternative or adjunctive diagnostic tool for breast cancer. This is pioneering data that reports on a novel diagnostic approach that is understudied and underreported. However, due to study limitations, the complexity of this technology, and the need for future development, biopsy still remains the current gold standard method of determining breast cancer type.
PubMed: 38024014
DOI: 10.7759/cureus.49015 -
Radiologia 2015To estimate and compare the diagnostic validity of tomosynthesis and digital mammography for screening and diagnosing breast cancer. (Review)
Review
OBJECTIVE
To estimate and compare the diagnostic validity of tomosynthesis and digital mammography for screening and diagnosing breast cancer.
MATERIAL AND METHODS
We systematically searched MedLine, EMBASE, and Web of Science for the terms breast cancer, screening, tomosynthesis, mammography, sensitivity, and specificity in publications in the period comprising June 2010 through February 2013. We included studies on diagnostic tests and systematic reviews. Two reviewers selected and evaluated the articles. We used QUADAS 2 to evaluate the risk of bias and the NICE criteria to determine the level of evidence. We compiled a narrative synthesis.
RESULTS
Of the 151 original studies identified, we selected 11 that included a total of 2475 women. The overall quality was low, with a risk of bias and follow-up and limitations regarding the applicability of the results. The level of evidence was not greater than level II. The sensitivity of tomosynthesis ranged from 69% to 100% and the specificity ranged from 54% to 100%. The negative likelihood ratio was good, and this makes tomosynthesis useful as a test to confirm a diagnosis. One-view tomosynthesis was no better than two-view digital mammography, and the evidence for the superiority of two-view tomosynthesis was inconclusive.
CONCLUSIONS
The results for the diagnostic validity of tomosynthesis in the diagnosis of breast cancer were inconclusive and there were no results for its use in screening.
Topics: Breast Neoplasms; Female; Humans; Mammography
PubMed: 25306860
DOI: 10.1016/j.rx.2014.06.006 -
Journal of Ultrasound Jun 2023The purpose of this study was to assess the diagnostic performance of mammography (MMG) and ultrasound (US) imaging for detecting breast cancer. (Meta-Analysis)
Meta-Analysis Review
PURPOSE
The purpose of this study was to assess the diagnostic performance of mammography (MMG) and ultrasound (US) imaging for detecting breast cancer.
METHODS
Comprehensive searches of PubMed, Scopus and EMBASE from 2008 to 2021 were performed. A summary receiver operating characteristic curve (SROC) was constructed to summarize the overall test performance of MMG and US. Histopathologic analysis and/or close clinical and imaging follow-up for at least 6 months were used as golden reference.
RESULTS
Analysis of the studies revealed that the overall validity estimates of MMG and US in detecting breast cancer were as follows: pooled sensitivity per-patient were 0.82 (95% CI 0.76-0.87) and 0.83 (95% CI 0.71-0.91) respectively, The pooled specificities for detection of breast cancer using MMG, and US were 0.84 (95% CI 0.73-0.92) and 0.84 (95% CI 0.74-0.91) respectively. AUC of MMG, and US were 0.8933 and 0.8310 respectively. Pooled sensitivity and specificity per-lesion was 76% (95% CI 0.62-0.86) and 82% (95% CI 0.66-0.91) for MMG and 94% (95% CI 0.87-0.97) and 84% (95% CI 0.74-0.91) for US.
CONCLUSIONS
The meta-analysis found that, US and MMG has similar diagnostic performance in detecting breast cancer on per-patient basis after corrected threshold effect. However, on a per-lesion basis US was found to have a better diagnostic accuracy than MMG.
Topics: Female; Humans; Breast Neoplasms; Mammography; Ultrasonography, Mammary; Ultrasonography; Sensitivity and Specificity
PubMed: 36696046
DOI: 10.1007/s40477-022-00755-3 -
Diagnostics (Basel, Switzerland) Aug 2022Contrast-enhanced mammography (CEM) and contrast-enhanced magnetic resonance imaging (CE-MRI) are commonly used in the screening of breast cancer. The present systematic... (Review)
Review
BACKGROUND
Contrast-enhanced mammography (CEM) and contrast-enhanced magnetic resonance imaging (CE-MRI) are commonly used in the screening of breast cancer. The present systematic review aimed to summarize, critically analyse, and meta-analyse the available evidence regarding the role of CE-MRI and CEM in the early detection, diagnosis, and preoperative assessment of breast cancer.
METHODS
The search was performed on PubMed, Google Scholar, and Web of Science on 28 July 2021 using the following terms "breast cancer", "preoperative staging", "contrast-enhanced mammography", "contrast-enhanced spectral mammography", "contrast enhanced digital mammography", "contrast-enhanced breast magnetic resonance imaging" "CEM", "CESM", "CEDM", and "CE-MRI". We selected only those papers comparing the clinical efficacy of CEM and CE-MRI. The study quality was assessed using the QUADAS-2 criteria. The pooled sensitivities and specificity of CEM and CE-MRI were computed using a random-effects model directly from the STATA "metaprop" command. The between-study statistical heterogeneity was tested (I-statistics).
RESULTS
Nineteen studies were selected for this systematic review. Fifteen studies (1315 patients) were included in the metanalysis. Both CEM and CE-MRI detect breast lesions with a high sensitivity, without a significant difference in performance (97% and 96%, respectively).
CONCLUSIONS
Our findings confirm the potential of CEM as a supplemental screening imaging modality, even for intermediate-risk women, including females with dense breasts and a history of breast cancer.
PubMed: 36010240
DOI: 10.3390/diagnostics12081890 -
Medical Journal of the Islamic Republic... 2023Breast cancer is a non-communicable and common disease that accounts for a high percentage of deaths. Early diagnosis of this disease reduces the death rate. Screening... (Review)
Review
BACKGROUND
Breast cancer is a non-communicable and common disease that accounts for a high percentage of deaths. Early diagnosis of this disease reduces the death rate. Screening methods such as digital mammography can help prevent or identify the disease earlier. Therefore, this study aims to analyze the cost-benefit of breast cancer using digital mammography.
METHODS
This systematic review was conducted based on PRISMA 2020 checklist. PubMed, Scopus, Web of Science, ProQuest, Cochrane Library, and Google Scholar were searched without any time limitation on June 2022. The quality of the studies was evaluated with the CHEERS checklist. After data extraction, the results were synthesized by thematic content analysis.
RESULTS
During the search, 3468 records were identified, of which 1061 were duplicates. 2407 titles and abstracts screened in terms of inclusion criteria. Finally, after studying 20 fulltexts, three of them were included in the study. The quality of these articles was scored between 10 and 16. These studies were from Spain, Denmark, and the United States from 2000 to 2019. Two studies showed that digital mammography is not as effective as other screening methods.
CONCLUSION
The results of this study showed that digital mammography is not very cost-benefit for the health care system. An increase in its repetition frequency imposes more costs on the health system and doesn't have more benefits for it.
PubMed: 37750094
DOI: 10.47176/mjiri.37.89 -
Breast (Edinburgh, Scotland) Oct 2021We conducted a systematic review and meta-analysis to compare the screening performance of synthesized mammography (SM) plus digital breast tomosynthesis (DBT) with... (Meta-Analysis)
Meta-Analysis Review
PURPOSE
We conducted a systematic review and meta-analysis to compare the screening performance of synthesized mammography (SM) plus digital breast tomosynthesis (DBT) with digital mammography (DM) plus DBT or DM alone.
METHODS
Medline, Embase, Web of Science, and the Cochrane Library databases were searched from January 2010 to January 2021. Eligible population-based studies on breast cancer screening comparing SM/DBT with DM/DBT or DM in asymptomatic women were included. A random-effect model was used in this meta-analysis. Data were summarized as risk differences (RDs), with 95 % confidence intervals (CIs).
RESULTS
Thirteen studies involving 1,370,670 participants were included. Compared with DM/DBT, screening using SM/DBT had similar breast cancer detection rate (CDR) (RD = -0.1/1000 screens, 95 % CI = -0.4 to 0.2, p = 0.557, I = 0 %), but lower recall rate (RD = -0.56 %, 95 % CI = -1.03 to -0.08, p = 0.022, I = 90 %) and lower biopsy rate (RD = -0.33 %, 95 % CI = -0.56 to -0.10, p = 0.005, I = 78 %). Compared with DM, SM/DBT improved CDR (RD = 2.0/1000 screens, 95 % CI = 1.4 to 2.6, p < 0.001, I = 63 %) and reduced recall rate (RD = -0.95 %, 95 % CI = -1.91 to -0.002, p = 0.049, I = 99 %). However, SM/DBT and DM had similar interval cancer rate (ICR) (RD = 0.1/1000 screens, 95 % CI = -0.6 to 0.8, p = 0.836, I = 71 %) and biopsy rate (RD = -0.05 %, 95 % CI = -0.35 to 0.24, p = 0.727, I = 93 %).
CONCLUSIONS
Screening using SM/DBT has similar breast cancer detection but reduces recall and biopsy when compared with DM/DBT. SM/DBT improves CDR when compared with DM, but they have little difference in ICR. SM/DBT could replace DM/DBT in breast cancer screening to reduce radiation dose.
Topics: Biopsy; Breast; Breast Neoplasms; Early Detection of Cancer; Female; Humans; Mammography; Mass Screening
PubMed: 34329948
DOI: 10.1016/j.breast.2021.07.016 -
International Journal of Environmental... Aug 2022It is well established that access to preventative care, such as breast or cervical cancer screening, can reduce morbidity and mortality. Certain groups may be missed... (Meta-Analysis)
Meta-Analysis Review
It is well established that access to preventative care, such as breast or cervical cancer screening, can reduce morbidity and mortality. Certain groups may be missed out of these healthcare services, such as women with disabilities, as they face many access barriers due to underlying inequalities and negative attitudes. However, the data have not been reviewed on whether women with disabilities face inequalities in the uptake of these services. A systematic review and meta-analysis were conducted to compare the uptake of breast and cervical cancer screening in women with and without disabilities. A search was conducted in July 2021 across four databases: PubMed, MEDLINE, Global Health, and CINAHL. Quantitative studies comparing the uptake of breast or cervical cancer screening between women with and without disabilities were eligible. Twenty-nine studies were included, all from high-income settings. One third of the 29 studies (34.5%, 10) were deemed to have a high risk of bias, and the remainder a low risk of bias. The pooled estimates showed that women with disabilities have 0.78 (95% CI: 0.72-0.84) lower odds of attending breast cancer screening and have 0.63 (95% CI: 0.45-0.88) lower odds of attending cervical cancer screening, compared to women without disabilities. In conclusion, women with disabilities face disparities in receipt of preventative cancer care. There is consequently an urgent need to evaluate and improve the inclusivity of cancer screening programs and thereby prevent avoidable morbidity and mortality.
Topics: Breast Neoplasms; Disabled Persons; Early Detection of Cancer; Female; Humans; Mammography; Mass Screening; Uterine Cervical Neoplasms
PubMed: 35954824
DOI: 10.3390/ijerph19159465 -
Insights Into Imaging Dec 2023Calcifications on mammography can be indicative of breast cancer, but the prognostic value of their appearance remains unclear. This systematic review and meta-analysis... (Review)
Review
BACKGROUND
Calcifications on mammography can be indicative of breast cancer, but the prognostic value of their appearance remains unclear. This systematic review and meta-analysis aimed to evaluate the association between mammographic calcification morphology descriptors (CMDs) and clinicopathological factors.
METHODS
A comprehensive literature search in Medline via Ovid, Embase.com, and Web of Science was conducted for articles published between 2000 and January 2022 that assessed the relationship between CMDs and clinicopathological factors, excluding case reports and review articles. The risk of bias and overall quality of evidence were evaluated using the QUIPS tool and GRADE. A random-effects model was used to synthesize the extracted data. This systematic review is reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA).
RESULTS
Among the 4715 articles reviewed, 29 met the inclusion criteria, reporting on 17 different clinicopathological factors in relation to CMDs. Heterogeneity between studies was present and the overall risk of bias was high, primarily due to small, inadequately described study populations. Meta-analysis demonstrated significant associations between fine linear calcifications and high-grade DCIS [pooled odds ratio (pOR), 4.92; 95% confidence interval (CI), 2.64-9.17], (comedo)necrosis (pOR, 3.46; 95% CI, 1.29-9.30), (micro)invasion (pOR, 1.53; 95% CI, 1.03-2.27), and a negative association with estrogen receptor positivity (pOR, 0.33; 95% CI, 0.12-0.89).
CONCLUSIONS
CMDs detected on mammography have prognostic value, but there is a high level of bias and variability between current studies. In order for CMDs to achieve clinical utility, standardization in reporting of CMDs is necessary.
CRITICAL RELEVANCE STATEMENT
Mammographic calcification morphology descriptors (CMDs) have prognostic value, but in order for CMDs to achieve clinical utility, standardization in reporting of CMDs is necessary.
SYSTEMATIC REVIEW REGISTRATION
CRD42022341599 KEY POINTS: • Mammographic calcifications can be indicative of breast cancer. • The prognostic value of mammographic calcifications is still unclear. • Specific mammographic calcification morphologies are related to lesion aggressiveness. • Variability between studies necessitates standardization in calcification evaluation to achieve clinical utility.
PubMed: 38051355
DOI: 10.1186/s13244-023-01529-z -
BMC Bioinformatics Oct 2023Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the...
BACKGROUND
Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the development of de novo areas in imaging science, such as radiomics and radiogenomics, have been adding more to personalize healthcare to stratify patients better. These techniques associate imaging phenotypes with the related disease genes. Various imaging modalities have been used for years to diagnose breast cancer. Nonetheless, digital breast tomosynthesis (DBT), a state-of-the-art technique, has produced promising results comparatively. DBT, a 3D mammography, is replacing conventional 2D mammography rapidly. This technological advancement is key to AI algorithms for accurately interpreting medical images.
OBJECTIVE AND METHODS
This paper presents a comprehensive review of deep learning (DL), radiomics and radiogenomics in breast image analysis. This review focuses on DBT, its extracted synthetic mammography (SM), and full-field digital mammography (FFDM). Furthermore, this survey provides systematic knowledge about DL, radiomics, and radiogenomics for beginners and advanced-level researchers.
RESULTS
A total of 500 articles were identified, with 30 studies included as the set criteria. Parallel benchmarking of radiomics, radiogenomics, and DL models applied to the DBT images could allow clinicians and researchers alike to have greater awareness as they consider clinical deployment or development of new models. This review provides a comprehensive guide to understanding the current state of early breast cancer detection using DBT images.
CONCLUSION
Using this survey, investigators with various backgrounds can easily seek interdisciplinary science and new DL, radiomics, and radiogenomics directions towards DBT.
Topics: Humans; Female; Deep Learning; Radiographic Image Enhancement; Breast; Breast Neoplasms; Mammography
PubMed: 37884877
DOI: 10.1186/s12859-023-05515-6 -
The Indian Journal of Radiology &... Jul 2024Although abundant literature is currently available on the use of deep learning for breast cancer detection in mammography, the quality of such literature is widely... (Review)
Review
Although abundant literature is currently available on the use of deep learning for breast cancer detection in mammography, the quality of such literature is widely variable. To evaluate published literature on breast cancer detection in mammography for reproducibility and to ascertain best practices for model design. The PubMed and Scopus databases were searched to identify records that described the use of deep learning to detect lesions or classify images into cancer or noncancer. A modification of Quality Assessment of Diagnostic Accuracy Studies (mQUADAS-2) tool was developed for this review and was applied to the included studies. Results of reported studies (area under curve [AUC] of receiver operator curve [ROC] curve, sensitivity, specificity) were recorded. A total of 12,123 records were screened, of which 107 fit the inclusion criteria. Training and test datasets, key idea behind model architecture, and results were recorded for these studies. Based on mQUADAS-2 assessment, 103 studies had high risk of bias due to nonrepresentative patient selection. Four studies were of adequate quality, of which three trained their own model, and one used a commercial network. Ensemble models were used in two of these. Common strategies used for model training included patch classifiers, image classification networks (ResNet in 67%), and object detection networks (RetinaNet in 67%). The highest reported AUC was 0.927 ± 0.008 on a screening dataset, while it reached 0.945 (0.919-0.968) on an enriched subset. Higher values of AUC (0.955) and specificity (98.5%) were reached when combined radiologist and Artificial Intelligence readings were used than either of them alone. None of the studies provided explainability beyond localization accuracy. None of the studies have studied interaction between AI and radiologist in a real world setting. While deep learning holds much promise in mammography interpretation, evaluation in a reproducible clinical setting and explainable networks are the need of the hour.
PubMed: 38912238
DOI: 10.1055/s-0043-1775737