Evaluation of average glandular dose and investigation of the relationship with compressed breast thickness in dual energy contrast enhanced digital mammography and digital breast tomosynthesis

A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer

OBJECTIVE

To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer.

METHODS

From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2-  and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered.

RESULTS

The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set.

CONCLUSIONS

The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.

Analysis of dual-energy mammography subtraction technique for the dose and image quality evaluation using 3D-printed breast phantom

This study aimed to quantitatively assess the radiation dose using XR-QA2 and the image quality of the dual-energy subtraction mammography technique on an in-house phantom. The analysis was carried out to investigate the effect of targets/filters on dose value and image quality using an in-house phantom made of PLA + as an object representing compressed breasts. All irradiation parameters were performed in the craniocaudal position with manual mode. Mean glandular dose (MGD) was recorded, followed by the calculation of the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and modulation transfer function (MTF) for image quality assessment parameters. The results showed that the image quality was accepted at dose levels within the IAEA and BAPETEN tolerance limit for 60 mm equivalent compressed breast using dual-energy mammography. Furthermore, the target/filter (W/Rh) reduced the dose by 1.03 mGy compared to the Mo/Mo and Mo/Rh with an enhancement in image quality. This indicated that the target/filter (W/Rh) combination was optimal due to the image quality improvement obtained with lower MGD.

Radiomics and artificial intelligence analysis by T2-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging to predict Breast Cancer Histological Outcome

OBJECTIVE

The objective of the study was to evaluate the accuracy of radiomics features obtained by MR images to predict Breast Cancer Histological Outcome.

METHODS

A total of 217 patients with malignant lesions were analysed underwent MRI examinations. Considering histological findings as the ground truth, four different types of findings were used in both univariate and multivariate analyses: (1) G1 + G2 vs G3 classification; (2) presence of human epidermal growth factor receptor 2 (HER2 + vs HER2 -); (3) presence of the hormone receptor (HR + vs HR -); and (4) presence of luminal subtypes of breast cancer.

RESULTS

The best accuracy for discriminating HER2 + versus HER2 - breast cancers was obtained considering nine predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 88% on validation set). The best accuracy for discriminating HR + versus HR - breast cancers was obtained considering nine predictors by T2-weighted subtraction images and a decision tree (accuracy of 90% on validation set). The best accuracy for discriminating G1 + G2 versus G3 breast cancers was obtained considering 16 predictors by early phase T1-weighted subtraction images in a linear regression model with an accuracy of 75%. The best accuracy for discriminating luminal versus non-luminal breast cancers was obtained considering 27 predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 94% on validation set).

CONCLUSIONS

The combination of radiomics analysis and artificial intelligence techniques could be used to support physician decision-making in prediction of Breast Cancer Histological Outcome.

Quantitative Study on the Breast Density and the Volume of the Mammary Gland According to the Patient's Age and Breast Quadrant

OBJECTIVES

Breast density is considered an independent risk factor for the development of breast cancer. This study aimed to quantitatively assess the percent breast density (PBD) and the mammary glands volume (MGV) according to the patient's age and breast quadrant. We propose a regression model to estimate PBD and MGV as a function of the patient's age.

METHODS

The breast composition in 1027 spiral breast CT (BCT) datasets without soft tissue masses, calcifications, or implants from 517 women (57 ± 8 years) were segmented. The breast tissue volume (BTV), MGV, and PBD of the breasts were measured in the entire breast and each of the four quadrants. The three breast composition features were analyzed in the seven age groups, from 40 to 74 years in 5-year intervals. A logarithmic model was fitted to the BTV, and a multiplicative inverse model to the MGV and PBD as a function of age was established using a least-squares method.

RESULTS

The BTV increased from 545 ± 345 to 676 ± 412 cm3, and the MGV and PBD decreased from 111 ± 164 to 57 ± 43 cm3 and from 21 ± 21 to 11 ± 9%, respectively, from the youngest to the oldest group (p < 0.05). The average PBD over all ages were 14 ± 13%. The regression models could predict the BTV, MGV, and PBD based on the patient's age with residual standard errors of 386 cm3, 67 cm3, and 13%, respectively. The reduction in MGV and PBD in each quadrant followed the ones in the entire breast.

CONCLUSIONS

The PBD and MGV computed from BCT examinations provide important information for breast cancer risk assessment in women. The study quantified the breast mammary gland reduction and density decrease over the entire breast. It established mathematical models to estimate the breast composition features-BTV, MGV, and PBD, as a function of the patient's age.

Barriers to Implementation of Contrast-Enhanced Mammography in Clinical Practice: AJR Expert Panel Narrative Review

Accumulating evidence shows that contrast-enhanced mammography (CEM) has higher diagnostic performance than digital mammography and ultrasound and comparable diagnostic performance to MRI for various indications. CEM also offers certain practical advantages for patients. Nevertheless, the clinical implementation of CEM has been limited because of a range of factors. This AJR Expert Panel Narrative Review explores such factors hindering CEM implementation. These factors include the following: the risks of iodinated contrast media, increased radiation exposure, indications for which CEM is not the preferred test or for which further evidence is needed, workflow adjustments needed when performing CEM examinations, incomplete availability of CEM-guided biopsy systems, and reimbursement challenges. Considerations that currently mitigate or are expected to mitigate these factors are also highlighted.

Evaluation of Contrast-Enhanced Mammography and Development of Flowchart for BI-RADS Classification of Breast Lesions

This study aimed to evaluate contrast-enhanced mammography (CEM) and to compare breast lesions on CEM and breast magnetic resonance imaging (MRI) using 5 features. We propose a flowchart for BI-RADS classification of breast lesions on CEM based on the Kaiser score (KS) flowchart for breast MRI. Sixty-eight subjects (women and men; median age 61.4 ± 11.6 years) who were suspected of having a malignant process in the breast based on digital mammography (MG) findings were included in the study. The patients underwent breast ultrasound (US), CEM, MRI and biopsy of the suspicious lesion. There were 47 patients with malignant lesions confirmed by biopsy and 21 patients with benign lesions, for each of which a KS was calculated. In the patients with malignant lesions, the MRI-derived KS was 9 (IQR 8-9); its CEM equivalent was 9 (IQR 8-9); and BI-RADS was 5 (IQR 4-5). In patients with benign lesions, MRI-derived KS was 3 (IQR 2-3); its CEM equivalent was 3 (IQR 1.7-5); and BI-RADS was 3 (IQR 0-4). There was no significant difference between the ROC-AUC of CEM and MRI (p = 0.749). In conclusion, there were no significant differences in KS results between CEM and breast MRI. The KS flowchart is useful for evaluating breast lesions on CEM.

Impact of age and breast thickness on mean glandular dose of standard digital mammography and digital breast tomosynthesis

This study compares the mean glandular dose (MGD) across 2D, 3D projection and Contrast-Enhanced Digital Mammography (CEDM) mammographic techniques. The important metadata were extracted from the digital mammography console. 650 subjects were clustered based on projections, age and CBT. The MGD of 2D, 3D, and CEDM was positively correlated with CBT but inversely correlated with the age factor. This study indicate MGD of CEDM was 16% and 22% lower compared to 2D and 3D techniques, respectively.

Radiation dose estimates based on Monte Carlo simulation for spiral breast computed tomography imaging in a large cohort of patients

BACKGROUND

Spiral breast computed tomography (BCT) equipped with a photon-counting detector (PCD) is a new radiological modality allowing for the compression-free acquisition of high-resolution 3-D datasets of the breast. Optimized dose exposu04170/re setups according to breast size were previously proposed but could not effectively be applied in a clinical environment due to ambiguity in measuring breast size.

PURPOSE

This study aims to report the standard radiation dose values in a large cohort of patients examined with BCT, and to provide a mathematical model to estimate radiation dose based on morphological features of the breast.

METHODS

This retrospective study was conducted on 1657 BCT examinations acquired between 2018 and 2021 from 829 participants (57 ± 10 years, all female). Applying a dedicated breast tissue segmentation algorithm and Monte Carlo (MC) simulation, mean absorbed dose (MAD), mean glandular dose (MGD), mean skin dose (MSD), maximum glandular dose (maxGD), and maximum skin dose (maxSD) were calculated and related to morphological features such as breast volume, effective diameter, breast length, skin volume, and glandularity. Effective dose (ED) was calculated by applying the corresponding beam and tissue weighting factors, 1 Sv/Gy and 0.12 per breast. Relevant morphological features predicting dose values were identified based on the Spearman's rank correlation coefficient. Exponential or bi-exponential models predicting the dose values as a function of morphological features were fitted by using a non-linear least squares (LS) method. The models were validated by assessing R² and residual standard error (RSE).

RESULTS

The most relevant morphological features for radiation dose estimation were the breast volume (correlation coefficient: -0.8), diameter (-0.7), and length (-0.6). The glandularity presented a weak-positive correlation (0.4) with MGD and maxGD due to the inhomogeneous distribution of the glandularity and absorbed dose in the 3-D breast volume. The standard MGDs were calculated to be 7.3 ± 0.7, 6.5 ± 0.3, and 5.9 ± 0.3 mGy, MADs to 7.6 ± 0.8, 6.8 ± 0.3, and 6.2 ± 0.3 mGy, maxSDs to 19.9 ± 1.6, 19.5 ± 0.5, and 18.9 ± 0.5 mGy, and EDs to 0.88 ± 0.08, 0.78 ± 0.04, and 0.72 ± 0.04 mSv for small, medium, and large breasts with average breast lengths of 5.9 ± 1.6, 8.7 ± 1.3, and 12.2 ± 2.0 cm, respectively. The estimated glandularity - 23.1 ± 16.9, 12.5 ± 11.4, and 6.9 ± 7.3% from small to large breasts. The mathematical models were able to estimate the MAD, MGD, MSD, and maxSD as a function of each morphological feature with only upto 0.5 mGy RSE.

CONCLUSION

We presented the typical morphological features and standard dose values according to the breast size acquired from a large patient cohort. We established radiation dose estimation models allowing accurate estimation of dose values including MGD with an acceptable RSE based on each of the easily measured morphological features of the breast. Clinicians could use the breast length to operate as a dosimetric alert of the scanner prior to a BCT scan. Radiation exposure for BCT was lower than diagnostic mammography (MG) and cone-beam breast CT (BCT).

Breast dosimetry in alternative X-ray-based imaging modalities used in current clinical practices

In X-ray breast imaging, Digital Mammography (DM) and Digital Breast Tomosynthesis (DBT), are the standard and largely used techniques, both for diagnostic and screening purposes. Other techniques, such as dedicated Breast Computed Tomography (BCT) and Contrast Enhanced Mammography (CEM) have been developed as an alternative or a complementary technique to the established ones. The performance of these imaging techniques is being continuously assessed to improve the image quality and to reduce the radiation dose. These imaging modalities are predominantly used in the diagnostic setting to resolve incomplete or indeterminate findings detected with conventional screening examinations and could potentially be used either as an adjunct or as a primary screening tool in select populations, such as for women with dense breasts. The aim of this review is to describe the radiation dosimetry for these imaging techniques, and to compare the mean glandular dose with standard breast imaging modalities, such as DM and DBT.

The Impact of Dense Breasts on the Stage of Breast Cancer at Diagnosis: A Review and Options for Supplemental Screening

The purpose of breast cancer screening is to find cancers early to reduce mortality and to allow successful treatment with less aggressive therapy. Mammography is the gold standard for breast cancer screening. Its efficacy in reducing mortality from breast cancer was proven in randomized controlled trials (RCTs) conducted from the early 1960s to the mid 1990s. Panels that recommend breast cancer screening guidelines have traditionally relied on the old RCTs, which did not include considerations of breast density, race/ethnicity, current hormone therapy, and other risk factors. Women do not all benefit equally from mammography. Mortality reduction is significantly lower in women with dense breasts because normal dense tissue can mask cancers on mammograms. Moreover, women with dense breasts are known to be at increased risk. To provide equity, breast cancer screening guidelines should be created with the goal of maximizing mortality reduction and allowing less aggressive therapy, which may include decreasing the interval between screening mammograms and recommending consideration of supplemental screening for women with dense breasts. This review will address the issue of dense breasts and the impact on the stage of breast cancer at the time of diagnosis, and discuss options for supplemental screening.

Radiomics in medical imaging: pitfalls and challenges in clinical management

BACKGROUND

Radiomics and radiogenomics are two words that recur often in language of radiologists, nuclear doctors and medical physicists especially in oncology field. Radiomics is the technique of medical images analysis to extract quantitative data that are not detected by human eye.

METHODS

This article is a narrative review on Radiomics in Medical Imaging. In particular, the review exposes the process, the limitations related to radiomics, and future prospects are discussed.

RESULTS

Several studies showed that radiomics is very promising. However, there were some critical issues: poor standardization and generalization of radiomics results, data-quality control, repeatability, reproducibility, database balancing and issues related to model overfitting.

CONCLUSIONS

Radiomics procedure should made considered all pitfalls and challenges to obtain robust and reproducible results that could be generalized in other patients cohort.

An update on radiomics techniques in primary liver cancers

BACKGROUND

Radiomics is a progressing field of research that deals with the extraction of quantitative metrics from medical images. Radiomic features detention indirectly tissue features such as heterogeneity and shape and can, alone or in combination with demographic, histological, genomic, or proteomic data, be used for decision support system in clinical setting.

METHODS

This article is a narrative review on Radiomics in Primary Liver Cancers. Particularly, limitations and future perspectives are discussed.

RESULTS

In oncology, assessment of tissue heterogeneity is of particular interest: genomic analysis have demonstrated that the degree of tumour heterogeneity is a prognostic determinant of survival and an obstacle to cancer control. Therefore, that Radiomics could support cancer detection, diagnosis, evaluation of prognosis and response to treatment, so as could supervise disease status in hepatocellular carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) patients. Radiomic analysis is a convenient radiological image analysis technique used to support clinical decisions as it is able to provide prognostic and / or predictive biomarkers that allow a fast, objective and repeatable tool for disease monitoring.

CONCLUSIONS

Although several studies have shown that this analysis is very promising, there is little standardization and generalization of the results, which limits the translation of this method into the clinical context. The limitations are mainly related to the evaluation of data quality, repeatability, reproducibility, overfitting of the model.

TRIAL REGISTRATION

Not applicable.

Impact of Obtaining a Digital Breast Tomosynthesis (DBT) Spot Compression View on Assessment of Equivocal DBT Findings

Background: A recently introduced DBT device allows obtaining DBT spot compression views using a small paddle during DBT acquisition. Objective: To evaluate the impact on diagnostic performance of obtaining a DBT spot compression view for assessment of equivocal DBT findings. Methods: This retrospective study included 102 women (mean age, 60 years) in whom a DBT spot compression view was obtained to characterize an equivocal finding on DBT at the performing radiologist's discretion, performed from December 14, 2018 to December 18, 2019. Two fellowship-trained breast radiologists and one breast imaging fellow independently reviewed examinations, aware of the location of the equivocal lesions. Readers first assigned a BI-RADS category using standard DBT views followed immediately by a category using the DBT spot compression view. BIRADS categories 2-3 were considered negative, and categories ≥4a were considered positive. Histology and at least 1 year of imaging follow-up served as reference standard. Intrareader agreement for one reader and interreader agreement among all readers were evaluated using kappa coefficients. Diagnostic performance was compared between DBT with and without DBT spot compression views using McNemar tests. Results: Intrareader agreement increased from 0.43 to 0.72, and interreader agreement increased from 0.21 to 0.45, based on kappa coefficients for DBT with and without spot compression views. Eighteen cancers were present. Compared with standard DBT views, DBT spot compression views yielded significantly increased accuracy for all three readers (74% vs 90%, 73% vs 94%, 71% vs 94%); significantly increased specificity for all three readers (69% vs 90%, 75% vs 94%; 68% vs 93%); and significantly increased sensitivity for one reader (67% vs 94%) without significant change in sensitivity for the two other readers (89% vs 100%; 100% vs 89%). Radiation dose was 1.97 mGy for the DBT spot compression view, versus 1.78-1.81 mGy for standard DBT craniocaudal and mediolateral oblique views. Conclusion: The DBT spot compression view increased intrareader agreement, interreader agreement, and diagnostic accuracy (primarily from improved specificity); the view's supplemental dose was slightly higher than that of a standard DBT view. Clinical impact: DBT spot compression may help characterize equivocal DBT findings, reducing further workup for benign findings.

Evaluation of Breast Galactography Using Digital Breast Tomosynthesis: A Clinical Exploratory Study

Objectives: To compare the application value of digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) in breast galactography. Materials and Methods: A total of 128 patients with pathological nipple discharge (PND) were selected to undergo galactography. DBT and FFDM were performed for each patient after injecting the contrast agent; the radiation dose of DBT and FFDM was calculated, and the image quality was evaluated in consensus by two senior breast radiologists. Histopathologic data were found in 49 of the 128 patients. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for both FFDM- and DBT-galactography were calculated using histopathologic results as a reference standard. Data were presented as percentages along with their 95% confidence intervals (CI). Results: The average age of the 128 patients was 46.53 years. The average glandular dose (AGD) of DBT-galactography was slightly higher than that of FFDM-galactography (p < 0.001). DBT-galactography was 30.7% higher than FFDM-galactography in CC view, while DBT-galactography increased by 21.7% compared with FFDM-galactography in ML view. Regarding catheter anatomic distortion, structure detail, and overall image quality groups, DBT scores were higher than FFDM scores, and the differences were significant for all measures (p < 0.05). In 49 patients with pathological nipple discharge, we found that the DBT-galactography had higher sensitivity, specificity, PPV, and NPV (93.3%, 75%, 97.7%, and 50%, respectively) than FFDM-galactography (91.1%, 50%, 95.3%, and 33.3%, respectively). Conclusions: Compared to FFDM-galactography, within the acceptable radiation dose range, DBT-galactography increases the sensitivity and specificity of lesion detection by improving the image quality, providing more confidence for the diagnosis of clinical ductal lesions.

Current Status of Contrast Enhanced Mammography: A Comprehensive Review

OBJECTIVES

The purpose of this article is to provide a detailed and updated review of the physics, techniques, indications, limitations, reporting, implementation and management of contrast enhanced mammography.

BACKGROUND

Contrast enhanced mammography (CEM), is an emerging iodine-based modified dual energy mammography technique. In addition to having the same advantages as standard full-field digital mammography (FFDM), CEM provides information regarding tumor enhancement, relying on tumor angiogenesis, similar to dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). This article reviews current literature on CEM and highlights considerations that are critical to the successful use of this modality.

CONCLUSION

Multiple studies point to the advantage of using CEM in the diagnostic setting of breast imaging, which approaches that of DCE-MRI.

Radiomics in hepatic metastasis by colorectal cancer

Background

Radiomics is an emerging field and has a keen interest, especially in the oncology field. The process of a radiomics study consists of lesion segmentation, feature extraction, consistency analysis of features, feature selection, and model building. Manual segmentation is one of the most critical parts of radiomics. It can be time-consuming and suffers from variability in tumor delineation, which leads to the reproducibility problem of calculating parameters and assessing spatial tumor heterogeneity, particularly in large or multiple tumors. Radiomic features provides data on tumor phenotype as well as cancer microenvironment. Radiomics derived parameters, when associated with other pertinent data and correlated with outcomes data, can produce accurate robust evidence based clinical decision support systems. The principal challenge is the optimal collection and integration of diverse multimodal data sources in a quantitative manner that delivers unambiguous clinical predictions that accurately and robustly enable outcome prediction as a function of the impending decisions.

Methods

The search covered the years from January 2010 to January 2021. The inclusion criterion was: clinical study evaluating radiomics of liver colorectal metastases. Exclusion criteria were studies with no sufficient reported data, case report, review or editorial letter.

Results

We recognized 38 studies that assessed radiomics in mCRC from January 2010 to January 2021. Twenty were on different tpics, 5 corresponded to most criteria; 3 are review, or letter to editors; so 10 articles were included.

Conclusions

In colorectal liver metastases radiomics should be a valid tool for the characterization of lesions, in the stratification of patients based on the risk of relapse after surgical treatment and in the prediction of response to chemotherapy treatment.