Diagnostic Accuracy Of Digital Mammography Research Articles

Diagnostic accuracy of digital mammography and breast ultrasonography in prediction of response to neoadjuvant systemic treatment in breast cancer patients

Aim: The aim of our study was to assess the accuracy of a combination of digital mammography and breast ultrasonography in the prediction of response to neoadjuvant systemic treatment in breast cancer patients with different tumor subtypes. Methods: The study was designed as a retrospective diagnostic accuracy study. Stage I-III female breast cancer patients who received any type of neoadjuvant systemic treatment with radiological response assessment by both mammography and breast ultrasound and followed by surgical treatment in the breast and axilla were included in the study. The primary outcome was the diagnostic accuracy of combined modalities of mammography and breast ultrasonography for predicting the pathological complete response. On mammography and breast ultrasonography, the radiological response was categorized into complete response and non-complete response. Pathological complete response on surgical specimens was described based on current guidelines. True and false positive cases as well as true and false negative cases were counted and compared among patients with 4 different molecular subtypes. The diagnostic accuracy of combined imaging modalities was analyzed for positive and negative predictive values, sensitivity, and specificity rates. All rates were calculated according to the previously described formulas. Results: Eighty-one breast cancer cases were included in the study. Positive predictive values of imaging were 100%, 75%, 100%, and 83%, whereas negative predictive values were 67%, 75%, 100%, and 100% in patients with HR+/HER2-, HR+/HER2+, HR-/HER2+ and HR-/HER2- tumors, respectively. Sensitivity rates were found to be 98%, 90%, 100%, and 100%, whereas specificity rates were 100%, 50%, 100%, and 67% in patients with HR+/HER2-, HR+/HER2+, HR-/HER2+ and HR-/HER2- tumors, respectively. Conclusion: Digital mammography and breast ultrasonography as a combined modality seem to show the pathological complete response after neoadjuvant systemic treatment in HR+/HER2- breast cancer patients with a very high specificity rate. Therefore, these conventional tools may help surgeons to select patients who might benefit from loco-regional treatment de-escalation with higher accuracy.

Performance evaluation of digital mammography, digital breast tomosynthesis and ultrasound in the detection of breast cancer using pathology as gold standard: an institutional experience

BackgroundMammography is the primary imaging modality for diagnosing breast cancer in women more than 40 years of age. Digital breast tomosynthesis (DBT), when supplemented with digital mammography (DM), is useful for increasing the sensitivity and improving BIRADS characterization by removing the overlapping effect. Ultrasonography (US), when combined with the above combination, further increases the sensitivity and diagnostic confidence. Since most of the research regarding tomosynthesis has been in screening settings, we wanted to quantify its role in diagnostic mammography. The purpose of this study was to assess the performance of DM alone vs. DM combined with DBT vs. DM plus DBT and ultrasound in diagnosing malignant breast neoplasms with the gold standard being histopathology or cytology.ResultsA prospective study of 1228 breasts undergoing diagnostic or screening mammograms was undertaken at our institute. Patients underwent 2 views DM, single view DBT and US. BIRADS category was updated after each step. Final categorization was made with all three modalities combined and pathological correlation was done for those cases in which suspicious findings were detected, i.e. 256 cases. Diagnosis based on pathology was done for 256 cases out of which 193 (75.4%) were malignant and the rest 63 (24.6%) were benign. The diagnostic accuracy of DM alone was 81.1%. Sensitivity, Specificity, PPV and NPV were 87.8%, 60%, 81.3% and 61.1%, respectively. With DM + DBT the diagnostic accuracy was 84.8%. Sensitivity, Specificity, PPV and NPV were 92%, 56.5%, 89% and 65%, respectively. The diagnostic accuracy of DM + DBT + US was found to be 85.1% and Sensitivity, Specificity, PPV and NPV were 96.3%, 50.7%, 85.7% and 82%, respectively.ConclusionThe combination of DBT to DM led to higher diagnostic accuracy, sensitivity and PPV. The addition of US to DM and DBT further increased the sensitivity and diagnostic accuracy and significantly increased the NPV even in diagnostic mammograms and should be introduced in routine practice for characterizing breast neoplasms.

Digital Mammography as a Screening Tool in Korea

국가암검진사업에서 매년 400만 명 이상의 여성이 유방촬영술을 이용한 유방암 검진을 받고 있다. 2000년 디지털 유방촬영술의 도입 이후, 선행 연구들에 의하면 디지털 유방촬영술은 치밀유방을 가진 여성에서 제한적으로 기존의 필름 방식 또는 computed radiography (이하 CR)보다 높은 진단 정확도를 보고하였다. 최근 국가암검진사업에서 수행된 자료를 분석한 결과에 따르면 디지털 유방촬영술의 진단 정확도가 필름 또는 CR 방식에 비해서 치밀유방을 가진 여성뿐만 아니라 모든 연령대의 여성에서 검진 횟수와 상관없이 보다 정확하였다. 우리나라는 OECD 국가 중에서도 높은 유방촬영기기 보급률에도 불구하고 현재 디지털 유방촬영기기의 보급은 전체 유방촬영기기 중, 35% 정도 수준으로 더디기만 하다. 디지털 유방촬영기기로의 신속한 전환을 위하여 수가제도의 개선, 유방 영상 판독 교육 지원 등 관련법과 제도의 정비가 필요할 것이다. 아울러 국가암검진사업에서 보다 많은 여성이 디지털 유방촬영기기를 이용한 유방암 검진을 받을 수 있도록 장비 보급의 지역 간 격차 해소를 위해 노력해야 할 것이다.

Diagnostic Accuracy of Digital Mammography in the Detection of Breast Cancer.

IntroductionBreast cancer has a high prevalence in the community and places very high demands on resources. Digital mammography provides a good quality image with reduced radiation dose and can detect breast carcinoma in its earlier stages, resulting in good prognosis and improved patient survival.ObjectiveTo calculate the diagnostic accuracy of digital mammography in the detection of breast cancer, using histopathology as a gold standard in women aged over 30 years, who are undergoing mammography for screening and diagnostic purposes.Materials and methodsThis was a cross-sectional analytical study, conducted in the department of radiology, for a total duration of 10 months. A total of 122 patients of age above 30 years, referred for digital mammography for the evaluation of different symptoms related to breast diseases, followed by biopsy/surgery and histopathology, were included in the study.ResultOur data confirmed that digital mammography is a highly accurate tool for breast cancer detection having a sensitivity of 97%, a specificity of 64.5%, a positive predictive value of 89%, and a negative predictive value of 90.9%, with a diagnostic accuracy of 89.3%.ConclusionConsidering our results, we recommend that digital mammography should replace screen-film mammography as a basic tool to detect breast cancer for both screening and diagnostic purposes.

A comparison of digital mammography detectors and emerging technology

The overall diagnostic accuracy of digital mammography in the context of screening has been shown to be similar or slightly better than screen-film mammography. However, digital mammography encompasses both Computed Radiography (CR) and integrated Digital Radiography (DR) and there is increasing evidence to suggest that differences in detector technology are associated with variations in cancer detection rate, dose and image quality. These differences are examined in detail.Although digital mammography offers many advantages compared to screen-film, there are still some limitations with its use as a screening tool and reduced cancer detection in dense breasts remains an issue. Digital mammography detectors have paved the way for emerging technologies which may offer improvements. Taking the definition of mammography to only include X-ray imaging of the breast, this article focuses on tomosynthesis, contrast-enhanced digital mammography, stereoscopic mammography and dedicated breast computed tomography. Advanced software applications such as Computed Aided Detection (CAD) and quantitative breast density assessment are also presented. The benefits and limitations of each technique are discussed.

Cancer Cases from ACRIN Digital Mammographic Imaging Screening Trial: Radiologist Analysis with Use of a Logistic Regression Model

To determine which factors contributed to the Digital Mammographic Imaging Screening Trial (DMIST) cancer detection results. This project was HIPAA compliant and institutional review board approved. Seven radiologist readers reviewed the film hard-copy (screen-film) and digital mammograms in DMIST cancer cases and assessed the factors that contributed to lesion visibility on both types of images. Two multinomial logistic regression models were used to analyze the combined and condensed visibility ratings assigned by the readers to the paired digital and screen-film images. Readers most frequently attributed differences in DMIST cancer visibility to variations in image contrast--not differences in positioning or compression--between digital and screen-film mammography. The odds of a cancer being more visible on a digital mammogram--rather than being equally visible on digital and screen-film mammograms--were significantly greater for women with dense breasts than for women with nondense breasts, even with the data adjusted for patient age, lesion type, and mammography system (odds ratio, 2.28; P < .0001). The odds of a cancer being more visible at digital mammography--rather than being equally visible at digital and screen-film mammography--were significantly greater for lesions imaged with the General Electric digital mammography system than for lesions imaged with the Fischer (P = .0070) and Fuji (P = .0070) devices. The significantly better diagnostic accuracy of digital mammography, as compared with screen-film mammography, in women with dense breasts demonstrated in the DMIST was most likely attributable to differences in image contrast, which were most likely due to the inherent system performance improvements that are available with digital mammography. The authors conclude that the DMIST results were attributable primarily to differences in the display and acquisition characteristics of the mammography devices rather than to reader variability.

Current state of digital mammography

Digital imaging is rapidly becoming modern radiology practice, resulting in the gradual replacement of conventional radiographs. At present high-quality digital mammography has been available for several years and is increasingly used for diagnostic and screening mammography. Some different digital mammography systems exist, which all have their advantages and disadvantages. Diagnostic accuracy of digital mammography has been shown to be at least equivalent to screen-film mammography (SFM). Digital mammography offers some potential advantages over SFM. This article describes the current state of digital mammography and presents data from clinical trials that support the use of digital mammography technology.

Full-Field Digital Versus Screen-Film Mammography: Comparative Accuracy in Concurrent Screening Cohorts

The purpose of this study was to compare the diagnostic accuracy of digital mammography with that of screen-film mammography in concurrent cohorts participating in the same population-based screening program. In a retrospective study covering 2004-2005, we compared digital with screen-film mammography in two concurrent screening cohorts of women 50-69 years old participating in a screening program operated from mobile units. Each cohort had 14,385 participants matched by age and interpreting radiologist from all participants consecutively registered. We compared recall and cancer detection rates. The recall rate was higher for digital mammography (4.56% vs 3.96%, p = 0.01), particularly when clustered microcalcifications were the only finding (1.05% vs 0.41%, p = 10(-6)) and for younger women (50-59 vs 60-69 years, 5.12% vs 4.17%, p = 0.009). The higher recall rate for digital mammography was mainly evident at incidence screening. The recall rate due to poor technical quality was lower with digital mammography (0.27% vs 0.50%, p = 0.002), possibly because real-time feedback was available. The detection rate was higher for digital mammography (0.72% vs 0.58%, p = 0.14), particularly for cancers depicted as clustered microcalcifications (0.26% vs 0.12%, p = 0.007), in younger (50-59 years) women (0.63% vs 0.42%, p = 0.09), and in denser breasts (1.09% vs 0.53%, p = 0.24). No significant difference was observed in positive predictive value on recall for digital mammography or screen-film mammography. Early cancer (pTis, pT1mic, pT1a) was more frequent in cancer detected with digital mammography than in that detected with screen-film mammography (41.3% vs 27.3%, p = 0.06). Digital mammography may be more effective than screen-film mammography in contemporary screening practice in mobile units. The data indicate that digital mammography depicts more tumors than does screen-film mammography, especially lesions seen as microcalcifications. The potential association with improved outcome warrants further study.

Digital mammography: what do we and what don't we know?

High-quality full-field digital mammography has been available now for several years and is increasingly used for both diagnostic and screening mammography. A number of different detector technologies exist, which all have their specific advantages and disadvantages. Diagnostic accuracy of digital mammography has been shown to be at least equivalent to film-screen mammography in a general screening population. Digital mammography is superior to screen-film mammography in younger women with dense breasts due to its ability to selectively optimize contrast in areas of dense parenchyma. This advantage is especially important in women with a genetic predisposition for breast cancer, where intensified early detection programs may have to start from 25 to 30 years of age. Tailored image processing and computer-aided diagnosis hold the potential to further improve the early detection of breast cancer. However, at present no consensus exists among radiologists on which processing is optimal for digital mammograms. Image processing may also vary significantly among vendors with so far limited interoperability. This review aims to summarize the available information regarding the impact of digital mammography on workflow and breast cancer diagnosis.

The Effects of Gray Scale Image Processing on Digital Mammography Interpretation Performance 1

To determine the effects of three image-processing algorithms on diagnostic accuracy of digital mammography in comparison with conventional screen-film mammography. A total of 201 cases consisting of nonprocessed soft copy versions of the digital mammograms acquired from GE, Fischer, and Trex digital mammography systems (1997-1999) and conventional screen-film mammograms of the same patients were interpreted by nine radiologists. The raw digital data were processed with each of three different image-processing algorithms creating three presentations-manufacturer's default (applied and laser printed to film by each of the manufacturers), MUSICA, and PLAHE-were presented in soft copy display. There were three radiologists per presentation. Area under the receiver operating characteristic curve for GE digital mass cases was worse than screen-film for all digital presentations. The area under the receiver operating characteristic for Trex digital mass cases was better, but only with images processed with the manufacturer's default algorithm. Sensitivity for GE digital mass cases was worse than screen film for all digital presentations. Specificity for Fischer digital calcifications cases was worse than screen film for images processed in default and PLAHE algorithms. Specificity for Trex digital calcifications cases was worse than screen film for images processed with MUSICA. Specific image-processing algorithms may be necessary for optimal presentation for interpretation based on machine and lesion type.

Diagnostic accuracy of digital mammography in patients with dense breasts who underwent problem-solving mammography: effects of image processing and lesion type.

To determine effects of lesion type (calcification vs mass) and image processing on radiologist's performance for area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for detection of masses and calcifications with digital mammography in women with mammographically dense breasts. This study included 201 women who underwent digital mammography at seven U.S. and Canadian medical centers. Three image-processing algorithms were applied to the digital images, which were acquired with Fischer, General Electric, and Lorad digital mammography units. Eighteen readers participated in the reader study (six readers per algorithm). Baseline values for reader performance with screen-film mammograms were obtained through the additional interpretation of 179 screen-film mammograms. A repeated-measures analysis of covariance allowing unequal slopes was used in each of the nine analyses (AUC, sensitivity, and specificity for each of three machines). Bonferroni correction was used. Although lesion type did not affect the AUC or sensitivity for Fischer digital images, it did affect specificity (P =.0004). For the General Electric digital images, AUC, sensitivity, and specificity were not affected by lesion type. For Lorad digital images, the results strongly suggested that lesion type affected AUC and sensitivity (P <.0001). None of the three image-processing methods tested affected the AUC, sensitivity, or specificity for the Fischer, General Electric, or Lorad digital images. Findings in this study indicate that radiologist's interpretation accuracy in interpreting digital mammograms depends on lesion type. Interpretation accuracy was not influenced by the image-processing method.