Technical and clinical breast cancer screening performance indicators for computed radiography versus direct digital radiography

Successes and challenges in extracting information from DICOM image databases for audit and research

In radiography, much valuable associated data (metadata) is generated during image acquisition. The current setup of picture archive and communication systems (PACS) can make extraction of this metadata difficult, especially as it is typically stored with the image. The aim of this work is to examine the current challenges in extracting image metadata and to discuss the potential benefits of using this rich information. This work focuses on breast screening, though the conclusions are applicable to other modalities.The data stored in PACS contain information, currently underutilised, and is of great benefit for auditing and improving imaging and radiographic practice. From the literature, we present examples of the potential clinical benefit such as audits of dose, and radiographic practice, as well as more advanced research highlighting the effects of radiographic practice, e.g. cancer detection rates affected by imaging technology.This review considers the challenges in extracting data, namely,• The search tools for data on most PACS are inadequate being both time-consuming and limited in elements that can be searched.• Security and information governance considerations• Anonymisation of data if required• Data curationThe review describes some solutions that have been successfully implemented.• Retrospective extraction: direct query on PACS• Extracting data prospectively• Use of structured reports• Use of trusted research environmentsUltimately, the data access process will be made easier by inclusion during PACS procurement. Auditing data from PACS can be used to improve quality of imaging and workflow, all of which will be a clinical benefit to patients.

Development of Adaptive Point-Spread Function Estimation Method in Various Scintillation Detector Thickness for X-ray Imaging

An indirect conversion X-ray detector uses a scintillator that utilizes the proportionality of the intensity of incident radiation to the amount of visible light emitted. A thicker scintillator reduces the patient's dose while decreasing the sharpness. A thin scintillator has an advantage in terms of sharpness; however, its noise component increases. Thus, the proposed method converts the spatial resolution of radiographic images acquired from a normal-thickness scintillation detector into a thin-thickness scintillation detector. Note that noise amplification and artifacts were minimized as much as possible after non-blind deconvolution. To accomplish this, the proposed algorithm estimates the optimal point-spread function (PSF) when the structural similarity index (SSIM) and feature similarity index (FSIM) are the most similar between thick and thin scintillator images. Simulation and experimental results demonstrate the viability of the proposed method. Moreover, the deconvolution images obtained using the proposed scheme show an effective image restoration method in terms of the human visible system compared to that of the traditional PSF measurement technique. Consequently, the proposed method is useful for restoring degraded images using the adaptive PSF while preventing noise amplification and artifacts and is effective in improving the image quality in the present X-ray imaging system.

Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: State of the art

Screening for breast cancer with mammography has been introduced in various countries over the last 30 years, initially using analog screen-film-based systems and, over the last 20 years, transitioning to the use of fully digital systems. With the introduction of digitization, the computer interpretation of images has been a subject of intense interest, resulting in the introduction of computer-aided detection (CADe) and diagnosis (CADx) algorithms in the early 2000's. Although they were introduced with high expectations, the potential improvement in the clinical realm failed to materialize, mostly due to the high number of false positive marks per analyzed image. In the last five years, the artificial intelligence (AI) revolution in computing, driven mostly by deep learning and convolutional neural networks, has also pervaded the field of automated breast cancer detection in digital mammography and digital breast tomosynthesis. Research in this area first involved comparison of its capabilities to that of conventional CADe/CADx methods, which quickly demonstrated the potential of this new technology. In the last couple of years, more mature and some commercial products have been developed, and studies of their performance compared to that of experienced breast radiologists are showing that these algorithms are on par with human-performance levels in retrospective data sets. Although additional studies, especially prospective evaluations performed in the real screening environment, are needed, it is becoming clear that AI will have an important role in the future breast cancer screening realm. Exactly how this new player will shape this field remains to be determined, but recent studies are already evaluating different options for implementation of this technology. The aim of this review is to provide an overview of the basic concepts and developments in the field AI for breast cancer detection in digital mammography and digital breast tomosynthesis. The pitfalls of conventional methods, and how these are, for the most part, avoided by this new technology, will be discussed. Importantly, studies that have evaluated the current capabilities of AI and proposals for how these capabilities should be leveraged in the clinical realm will be reviewed, while the questions that need to be answered before this vision becomes a reality are posed.

Geant4 physics list comparison for the simulation of phase-contrast mammography (XPulse project)

PURPOSE

Breast cancer is the most frequent cancer in women. Early and accurate detection of the disease is a major factor in patient survival. To this end, phase-contrast imaging has gained significant interest in recent years. The aim of this work was to validate the physics models of a Geant4 mammography imaging simulation (in the context of the XPulse project) by comparing to EGSnrc results.

METHODS

We used three Geant4 electromagnetic physics lists of the version 10.4 of the toolkit: Standard, Livermore and Penelope. We calculated energy distributions in homogeneous and inhomogeneous phantoms and breast doses in DICOM images. The simulations used photon beams of energies 20-100 keV. The Geant4 calculations were compared with EGSnrc/DOSXYZnrc simulations.

RESULTS

We found a very good agreement between the Standard Electromagnetic option 4 and Livermore Physics Lists (within 1% for all beam energies). Larger differences were found between Standard Electromagnetic option 4 and Penelope Physics Lists (about 4%). The agreement of longitudinal energy distributions between Geant4 Standard Electromagnetic option 4 and EGSnrc was good in water and light biological materials, but important discrepancies were found in heavy elements. We confirmed with both codes that dose to the breast is minimal at beam energy around 60 keV.

CONCLUSIONS

Overall, we found good agreement between the option 4 of the Standard Electromagnetic physics list and Livermore physics lists of Geant4, as well as EGSnrc for materials relevant to mammography screening. Further investigations are needed for the case of heavier materials.

Characterization of radiographers' mammography practice in five European countries: a pilot study

Objectives

This pilot study aimed to characterize and compare radiographers’ mammography practice, including quality control and continuous professional development in five European countries.

Methods

Online survey was performed to collect data regarding participants’ profile, institution’s profile, mammography practice, quality control and continuous professional development. The questionnaire was sent to clinical radiographers working in Estonia, Finland, Norway, Portugal and Switzerland. Descriptive statistical and subgroup analyzes were performed.

Results

The amount of returned questionnaires was 140. Most respondents were female (92%), having radiography bachelor. The majority (89%) of radiographers was working with full-field digital mammography. The majority (97%) of mammography images were acquired using AEC, and half of the radiographers were using dose saving programmes suggested by the manufacturers. The most typical (50%) compression force ranged from 8 to 11 kg. Part of the radiographers (44%) did not know if their practice followed specific guidelines. The most challenging tasks in mammography identified by radiographers were patient positioning (86%), coping with pain (88%), managing anxiety (83%) and imaging breast implants (71%). The majority (88%) of the respondents undertook continuous professional development activities.

Conclusions

The mammography practice varies across the five countries. We found country-specific traits related to mammography image acquisition, patient-centered care and quality management procedures. The lack of evidence-based knowledge suggests the importance of well-designed studies on these topics. The variability found in this pilot study encourages radiographers to question their own practice and teachers to review and revise the training programmes. Validation in larger studies including more countries is needed.

Mammographic density assessed on paired raw and processed digital images and on paired screen-film and digital images across three mammography systems

BACKGROUND

Inter-women and intra-women comparisons of mammographic density (MD) are needed in research, clinical and screening applications; however, MD measurements are influenced by mammography modality (screen film/digital) and digital image format (raw/processed). We aimed to examine differences in MD assessed on these image types.

METHODS

We obtained 1294 pairs of images saved in both raw and processed formats from Hologic and General Electric (GE) direct digital systems and a Fuji computed radiography (CR) system, and 128 screen-film and processed CR-digital pairs from consecutive screening rounds. Four readers performed Cumulus-based MD measurements (n = 3441), with each image pair read by the same reader. Multi-level models of square-root percent MD were fitted, with a random intercept for woman, to estimate processed-raw MD differences.

RESULTS

Breast area did not differ in processed images compared with that in raw images, but the percent MD was higher, due to a larger dense area (median 28.5 and 25.4 cm2 respectively, mean √dense area difference 0.44 cm (95% CI: 0.36, 0.52)). This difference in √dense area was significant for direct digital systems (Hologic 0.50 cm (95% CI: 0.39, 0.61), GE 0.56 cm (95% CI: 0.42, 0.69)) but not for Fuji CR (0.06 cm (95% CI: -0.10, 0.23)). Additionally, within each system, reader-specific differences varied in magnitude and direction (p < 0.001). Conversion equations revealed differences converged to zero with increasing dense area. MD differences between screen-film and processed digital on the subsequent screening round were consistent with expected time-related MD declines.

CONCLUSIONS

MD was slightly higher when measured on processed than on raw direct digital mammograms. Comparisons of MD on these image formats should ideally control for this non-constant and reader-specific difference.

Detection Rate, Recall Rate, and Positive Predictive Value of Digital Compared to Screen-Film Mammography in the Quebec Population-Based Breast Cancer Screening Program

PURPOSE

The study sought to compare performance indicators of computed radiography (CR) using different plate readers, digital direct radiography (DR), and screen-film mammography (SFM) in a population-based screening program.

METHODS

This analysis involved women 50-69 years of age who participated in the breast screening program of Quebec (Canada) and who had screening mammogram between January 1, 2007, and September 30, 2012. The detection rate, recall rate, and positive predictive value of CR (n = 672,125 mammograms) and DR (n = 60,023) were compared to SFM (n = 782,894) using mixed-effect logistic regression, adjusting for potential confounders. No institutional review board approval was required.

RESULTS

CR was not associated with change in cancer detection rate (odds ratio [OR]: 0.95; 95% confidence interval [CI]: 0.88-1.03), but with a small increase in recall rate (OR: 1.03; 95% CI: 1.01-1.06) compared to SFM. The association of CR with recall rate varies with the CR plate reader manufacturer (P < .0001). DR was not associated with change in detection rate (OR: 1.06; 95% CI: 0.89-1.25), but with an increase in the recall rate (OR: 1.25; 95% CI: 1.19-1.30) compared to SFM.

CONCLUSIONS

In our screening program, digital mammograms gave detection rates equivalent to those of SFM, but with an increase of recall rate, particularly for DR. If this situation persists, the adoption of DR may increase the adverse effects of screening with little or no benefit for women.

The relationship between cancer detection in mammography and image quality measurements

PURPOSE

To investigate the relationship between image quality measurements and the clinical performance of digital mammographic systems.

METHODS

Mammograms containing subtle malignant non-calcification lesions and simulated malignant calcification clusters were adapted to appear as if acquired by four types of detector. Observers searched for suspicious lesions and gave these a malignancy score. Analysis was undertaken using jackknife alternative free-response receiver operating characteristics weighted figure of merit (FoM). Images of a CDMAM contrast-detail phantom were adapted to appear as if acquired using the same four detectors as the clinical images. The resultant threshold gold thicknesses were compared to the FoMs using a linear regression model and an F-test was used to find if the gradient of the relationship was significantly non-zero.

RESULTS

The detectors with the best image quality measurement also had the highest FoM values. The gradient of the inverse relationship between FoMs and threshold gold thickness for the 0.25mm diameter disk was significantly different from zero for calcification clusters (p=0.027), but not for non-calcification lesions (p=0.11). Systems performing just above the minimum image quality level set in the European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis resulted in reduced cancer detection rates compared to systems performing at the achievable level.

CONCLUSIONS

The clinical effectiveness of mammography for the task of detecting calcification clusters was found to be linked to image quality assessment using the CDMAM phantom. The European Guidelines should be reviewed as the current minimum image quality standards may be too low.

Breast cancer detection rates using four different types of mammography detectors

OBJECTIVE

To compare the performance of different types of detectors in breast cancer detection.

METHODS

A mammography image set containing subtle malignant non-calcification lesions, biopsy-proven benign lesions, simulated malignant calcification clusters and normals was acquired using amorphous-selenium (a-Se) detectors. The images were adapted to simulate four types of detectors at the same radiation dose: digital radiography (DR) detectors with a-Se and caesium iodide (CsI) convertors, and computed radiography (CR) detectors with a powder phosphor (PIP) and a needle phosphor (NIP). Seven observers marked suspicious and benign lesions. Analysis was undertaken using jackknife alternative free-response receiver operating characteristics weighted figure of merit (FoM). The cancer detection fraction (CDF) was estimated for a representative image set from screening.

RESULTS

No significant differences in the FoMs between the DR detectors were measured. For calcification clusters and non-calcification lesions, both CR detectors' FoMs were significantly lower than for DR detectors. The calcification cluster's FoM for CR NIP was significantly better than for CR PIP. The estimated CDFs with CR PIP and CR NIP detectors were up to 15% and 22% lower, respectively, than for DR detectors.

CONCLUSION

Cancer detection is affected by detector type, and the use of CR in mammography should be reconsidered.

KEY POINTS

The type of mammography detector can affect the cancer detection rates. CR detectors performed worse than DR detectors in mammography. Needle phosphor CR performed better than powder phosphor CR. Calcification clusters detection is more sensitive to detector type than other cancers.

[Explorations of breast microcalcifications: Guidelines]

OBJECTIVES

To assess imaging performances for the detection, characterization and biopsy of breast microcalcifications and make recommendations.

MATERIALS AND METHODS

French and English publications were searched using PubMed, Cochrane Library and international learned societies recommendations.

RESULTS

Digital mammography (DR [Direct Radiography] and CR [Computed Radiography]) and screen-film mammography demonstrate good performances for the detection and the characterization of breast microcalcifications. Systematic use of the 2013 edition of the BI-RADS lexicon is recommended for description and characterization of microcalcifications. Faced with BI-RADS 4 or 5 microcalcifications, breast ultrasound is recommended but a normal result does not eliminate the diagnosis of cancer and other examination should be performed. Literature review does not allow recommending digital breast tomosynthesis, elastography or MRI to analyze microcalcifications. In case of probably benign microcalcifications (BI-RADS 3), six months, one year and at least two years follow-up are recommended. In case a biopsy is indicated, it is recommended to use a vacuum-assisted macrobiopsy system with 11-gauges needles or bigger. If no calcification is visible on the radiography of the specimen, it is recommended to obtain additional samples.

Getting started with protocol for quality assurance of digital mammography in the clinical centre of Montenegro

The purpose of this work is (i) to work out a test procedure for quality assurance (QA) in digital mammography with newly released test equipment, including the MagicMax mam multimeter (IBA, Germany) and the anthropomorphic tissue equivalent phantom Mammo AT (IBA, Germany), and (ii) to determine whether a first digital computer radiography (CR) system in Montenegro meets the current European standards. Tested parameters were tube output (µGy mAs(-1)) and output rate (mGy s(-1)), reproducibility and accuracy of tube voltage, half value layer, reproducibility and accuracy of the AEC system, exposure control steps, image receptor's response function, image quality and printer stability test. The evaluated dosimetric quantity is the average glandular dose (AGD) as evaluated from PMMA slabs simulating breast tissue. The main findings are that QA can be organised in Montenegro. (1) All measured parameters are within the range described in European protocols except the tube voltage which deviated more than ± 1 kV. The automatic determination of the HVL was satisfactorily. AGD ranged from 0.66 to 7.02 mGy for PMMA thicknesses from 20 to 70 mm, and is in accordance with literature data. (2) The image quality score as obtained with the anthropomorphic tissue equivalent phantom Mammo AT for the CR system was similar to findings on the authors' conventional screen-film mammography. (3) In clinical practice the mammograms are printed. The CR reader produces images with a pixel size of 43.75 µm, which is compatible with the laser printer (39 µm laser spot spacing). The image processing algorithm embedded in the reader successfully processes mammograms with desirable image brightness and contrast in the printed image. The authors conclude that this first digital mammography system seems a good candidate for breast cancer screening applications.

Digital compared to screen-film mammography: breast cancer prognostic features in an organized screening program

Our previous study found cancer detection rates were equivalent for direct radiography compared to screen-film mammography, while rates for computed radiography were significantly lower. This study compares prognostic features of invasive breast cancers by type of mammography. Approved by the University of Toronto Research Ethics Board, this study identified invasive breast cancers diagnosed among concurrent cohorts of women aged 50-74 screened by direct radiography, computed radiography, or screen-film mammography from January 1, 2008 to December 31, 2009. During the study period, 816,232 mammograms were performed on 668,418 women, and 3,323 invasive breast cancers were diagnosed. Of 2,642 eligible women contacted, 2,041 participated (77.3 %). The final sample size for analysis included 1,405 screen-detected and 418 interval cancers (diagnosed within 24 months of a negative screening mammogram). Polytomous logistic regression was performed to evaluate the association between tumour characteristics and type of mammography, and between tumour characteristics and detection method. Odds ratios (OR) and 95 % confidence intervals (CI) were recorded. Cancers detected by computed radiography compared to screen-film mammography were significantly more likely to be lymph node positive (OR 1.94, 95 %CI 1.01-3.73) and have higher stage (II:I, OR 2.14, 95 %CI 1.11-4.13 and III/IV:I, OR 2.97, 95 %CI 1.02-8.59). Compared to screen-film mammography, significantly more cancers detected by direct radiography (OR 1.64, 95 %CI 1.12-2.38) were lymph node positive. Interval cancers had worse prognostic features compared to screen-detected cancers, irrespective of mammography type. Screening with computed radiography may lead to the detection of cancers with a less favourable stage distribution compared to screen-film mammography that may reflect a delayed diagnosis. Screening programs should re-evaluate their use of computed radiography for breast screening.