In Vivo and Post-mortem Comparisons of IVIM/Time-dependent Diffusion MR Imaging Parameters in Melanoma and Breast Cancer Xenograft Models.

PURPOSE: We aimed to investigate the changes in intravoxel incoherent motion (IVIM) and diffusion parameters between in vivo and post-mortem conditions and the time dependency of these parameters using two different mouse tumor models with different vessel lumen sizes. METHODS: Six B16 and six MDA-MB-231 xenograft mice were scanned using 7 Tesla MRI under both in vivo/post-mortem conditions. Diffusion weighted imaging with 17 b-values (0-3000 s/mm2) were obtained at two diffusion times (9 and 27.6 ms). The shifted apparent diffusion coefficient (sADC) using 2 b-values (200 and 1500 s/mm2), non-Gaussian diffusion and IVIM parameters (ADC0, K, fIVIM) were estimated at each of the diffusion times. The results were evaluated by repeated measures two-way analysis of variance and post hoc Bonferroni test. RESULTS: In B16 tumors, fIVIM significantly decreased with post-mortem conditions (from 12.6 ± 6.5% to 5.2 ± 1.9%, P < 0.05 at long diffusion time; from 11.0 ± 2.4% to 4.6 ± 2.7%, P < 0.05 at short diffusion time). In MDA-MB-231 tumors, fIVIM also significantly decreased (from 8.8 ± 3.8% to 2.6 ± 1.1%, P < 0.05 at long; from 7.9 ± 5.4% to 2.9 ± 1.1%, P < 0.05 at short). No diffusion time dependency was observed (P = 0.59 in B16 and P = 0.77 in MDA-MB-231). The sADC and ADC0 values tended to decrease and the K value tended to increase after sacrificing and when increasing the diffusion time. CONCLUSION: The fIVIM values dropped after sacrificing, confirming that IVIM MRI is a promising quantitative parameter to evaluate blood microcirculation. The presence of residual post-mortem fIVIM values suggested that the influence of water molecule diffusion in the blood lumen may contribute to the IVIM effect. Diffusion MRI parameter's time dependency and those changes after sacrificing could possibly provide additional insights into diffusion hindrance mechanisms.

Non-surgical ablation for breast cancer: an emerging therapeutic option.

Non-surgical ablation is emerging as an alternative local therapy option for patients with early-stage breast cancer and encompasses two main types of percutaneous therapeutic procedures: radiofrequency ablation and cryoablation. Both techniques involve obliteration of a spherical lesion and feasibility studies have shown that complete tumour ablation is achievable with good or excellent cosmetic results. Although few clinical studies have directly compared non-surgical ablation with conventional surgical resection, observational studies indicate that clinical outcomes are favourable with acceptable rates of local control and no detriment to long-term survival. There remain outstanding issues with these percutaneous ablative techniques that require resolution before they could be incorporated into routine clinical practice. Hence, a consensus meeting was convened to discuss the challenges of non-surgical ablation and clarify indications for its use alongside clinical management pathways. In this Policy Review we will address some of the broader biological aspects of non-surgical ablation, including immune-modulatory effects and potential novel applications for the future.

The Japanese breast cancer society clinical practice guidelines for breast cancer screening and diagnosis, 2022 edition.

This article provides updates to readers based on the newly published Japanese Breast Cancer Society Clinical Practice Guidelines for Breast Cancer Screening and Diagnosis, 2022 Edition. These guidelines incorporate the latest evaluation of evidence from studies of diagnostic accuracy. For each clinical question, outcomes for benefits and harms were established, and qualitative or quantitative systematic reviews were conducted. Recommendations were determined through voting by a multidisciplinary group, and guidelines were documented to facilitate shared decision-making among patients and medical professionals. The guidelines address screening, surveillance, and pre- and postoperative diagnosis of breast cancer. In an environment that demands an integrated approach, decisions are needed on how to utilize modalities, such as mammography, ultrasound, MRI, and PET/CT. Additionally, it is vital to understand the appropriate use of new technologies, such as tomosynthesis, elastography, and contrast-enhanced ultrasound, and to consider how best to adapt these methods for individual patients.

Multiparametric Approach to Breast Cancer With Emphasis on Magnetic Resonance Imaging in the Era of Personalized Breast Cancer Treatment.

ABSTRACT: A multiparametric approach to breast cancer imaging offers the advantage of integrating the diverse contributions of various parameters. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the most important MRI sequence for breast imaging. The vascularity and permeability of lesions can be estimated through the use of semiquantitative and quantitative parameters. The increased use of ultrafast DCE-MRI has facilitated the introduction of novel kinetic parameters. In addition to DCE-MRI, diffusion-weighted imaging provides information associated with tumor cell density, with advanced diffusion-weighted imaging techniques such as intravoxel incoherent motion, diffusion kurtosis imaging, and time-dependent diffusion MRI opening up new horizons in microscale tissue evaluation. Furthermore, T2-weighted imaging plays a key role in measuring the degree of tumor aggressiveness, which may be related to the tumor microenvironment. Magnetic resonance imaging is, however, not the only imaging modality providing semiquantitative and quantitative parameters from breast tumors. Breast positron emission tomography demonstrates superior spatial resolution to whole-body positron emission tomography and allows comparable delineation of breast cancer to MRI, as well as providing metabolic information, which often precedes vascular and morphological changes occurring in response to treatment. The integration of these imaging-derived factors is accomplished through multiparametric imaging. In this article, we explore the relationship among the key imaging parameters, breast cancer diagnosis, and histological characteristics, providing a technical and theoretical background for these parameters. Furthermore, we review the recent studies on the application of multiparametric imaging to breast cancer and the significance of the key imaging parameters.

Ultrafast Dynamic Contrast-Enhanced MRI of the Breast: From Theory to Practice.

The development of ultrafast dynamic contrast-enhanced (UF-DCE) MRI has occurred in tandem with fast MRI scan techniques, particularly view-sharing and compressed sensing. Understanding the strengths of each technique and optimizing the relevant parameters are essential to their implementation. UF-DCE MRI has now shifted from research protocols to becoming a part of clinical scan protocols for breast cancer. UF-DCE MRI is expected to compensate for the low specificity of abbreviated MRI by adding kinetic information from the upslope of the time-intensity curve. Because kinetic information from UF-DCE MRI is obtained from the shape and timing of the initial upslope, various new kinetic parameters have been proposed. These parameters may be associated with receptor status or prognostic markers for breast cancer. In addition to the diagnosis of malignant lesions, more emphasis has been placed on predicting and evaluating treatment response because hyper-vascularity is linked to the aggressiveness of breast cancers. In clinical practice, it is important to note that breast lesion images obtained from UF-DCE MRI are slightly different from those obtained by conventional DCE MRI in terms of morphology. A major benefit of using UF-DCE MRI is avoidance of the marked or moderate background parenchymal enhancement (BPE) that can obscure the target enhancing lesions. BPE is less prominent in the earlier phases of UF-DCE MRI, which offers better lesion-to-noise contrast. The excellent contrast of early-enhancing vessels provides a key to understanding the detailed pathological structure of tumor-associated vessels. UF-DCE MRI is normally accompanied by a large volume of image data for which automated/artificial intelligence-based processing is expected to be useful. In this review, both the theoretical and practical aspects of UF-DCE MRI are summarized. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

A multiparametric approach to predict triple-negative breast cancer including parameters derived from ultrafast dynamic contrast-enhanced MRI.

OBJECTIVE: Triple-negative breast cancer (TNBC) is a highly proliferative breast cancer subtype. We aimed to identify TNBC among invasive cancers presenting as masses using maximum slope (MS) and time to enhancement (TTE) measured on ultrafast (UF) DCE-MRI, ADC measured on DWI, and rim enhancement on UF DCE-MRI and early-phase DCE-MRI. METHODS: This retrospective single-center study, between December 2015 and May 2020, included patients with breast cancer presenting as masses. Early-phase DCE-MRI was performed immediately after UF DCE-MRI. Interrater agreements were evaluated using the intraclass correlation coefficient (ICC) and Cohen's kappa. Univariate and multivariate logistic regression analyses of the MRI parameters, lesion size, and patient age were performed to predict TNBC and create a prediction model. The programmed death-ligand 1 (PD-L1) expression statuses of the patients with TNBCs were also evaluated. RESULTS: In total, 187 women (mean age, 58 years ± 12.9 [standard deviation]) with 191 lesions (33 TNBCs) were evaluated. The ICC for MS, TTE, ADC, and lesion size were 0.95, 0.97, 0.83, and 0.99, respectively. The kappa values of rim enhancements on UF and early-phase DCE-MRI were 0.88 and 0.84, respectively. MS on UF DCE-MRI and rim enhancement on early-phase DCE-MRI remained significant parameters after multivariate analyses. The prediction model created using these significant parameters yielded an area under the curve of 0.74 (95% CI, 0.65, 0.84). The PD-L1-expressing TNBCs tended to have higher rim enhancement rates than the non-PD-L1-expressing TNBCs. CONCLUSION: A multiparametric model using UF and early-phase DCE-MRI parameters may be a potential imaging biomarker to identify TNBCs. CLINICAL RELEVANCE STATEMENT: Prediction of TNBC or non-TNBC at an early point of diagnosis is crucial for appropriate management. This study offers the potential of UF and early-phase DCE-MRI to offer a solution to this clinical issue. KEY POINTS: • It is crucial to predict TNBC at an early clinical period. • Parameters on UF DCE-MRI and early-phase conventional DCE-MRI help in predicting TNBC. • Prediction of TNBC by MRI may be useful in determining appropriate clinical management.

Performance of dedicated breast PET in breast cancer screening: comparison with digital mammography plus digital breast tomosynthesis and ultrasound.

OBJECTIVE: To compare the diagnostic performance of dedicated breast positron emission tomography (dbPET) in breast cancer screening with digital mammography plus digital breast tomosynthesis (DM-DBT) and breast ultrasound (US). METHODS: Women who participated in opportunistic whole-body PET/computed tomography cancer screening programs with breast examinations using dbPET, DM-DBT, and US between 2016-2020, whose results were determined pathologically or by follow-up for at least 1 year, were included. DbPET, DM-DBT, and US assessments were classified into four diagnostic categories: A (no abnormality), B (mild abnormality), C (need for follow-up), and D (recommend further examination). Category D was defined as screening positive. Each modality's recall rate, sensitivity, specificity, and positive predictive value (PPV) were calculated per examination to evaluate their diagnostic performance for breast cancer. RESULTS: Out of 2156 screenings, 18 breast cancer cases were diagnosed during the follow-up period (10 invasive cancers and eight ductal carcinomas in situ [DCIS]). The recall rates for dbPET, DM-DBT, and US were 17.8%, 19.2%, and 9.4%, respectively. The recall rate of dbPET was highest in the first year and subsequently decreased to 11.4%. dbPET, DM-DBT, and US had sensitivities of 72.2%, 88.9%, and 83.3%; specificities of 82.6%, 81.4%, and 91.2%; and PPVs of 3.4%, 3.9%, and 7.4%, respectively. The sensitivities of dbPET, DM-DBT, and US for invasive cancers were 90%, 100%, and 90%, respectively. There were no significant differences between the modalities. One case of dbPET-false-negative invasive cancer was identified in retrospect. DbPET had 50% sensitivity for DCIS, while that of both DM-DBT and US was 75%. Furthermore, the specificity of dbPET in the first year was the lowest among all periods, and modalities increased over the years to 88.7%. The specificity of dbPET was significantly higher than that of DM-DBT (p < 0.01) in the last 3 years. CONCLUSIONS: DbPET had a compatible sensitivity to DM-DBT and breast US for invasive breast cancer. The specificity of dbPET was improved and became higher than that of DM-DBT. DbPET may be a feasible screening modality.

Feasibility of Dedicated Breast Positron Emission Tomography Image Denoising Using a Residual Neural Network.

Objectives: This study aimed to create a deep learning (DL)-based denoising model using a residual neural network (Res-Net) trained to reduce noise in ring-type dedicated breast positron emission tomography (dbPET) images acquired in about half the emission time, and to evaluate the feasibility and the effectiveness of the model in terms of its noise reduction performance and preservation of quantitative values compared to conventional post-image filtering techniques. Methods: Low-count (LC) and full-count (FC) PET images with acquisition durations of 3 and 7 minutes, respectively, were reconstructed. A Res-Net was trained to create a noise reduction model using fifteen patients' data. The inputs to the network were LC images and its outputs were denoised PET (LC + DL) images, which should resemble FC images. To evaluate the LC + DL images, Gaussian and non-local mean (NLM) filters were applied to the LC images (LC + Gaussian and LC + NLM, respectively). To create reference images, a Gaussian filter was applied to the FC images (FC + Gaussian). The usefulness of our denoising model was objectively and visually evaluated using test data set of thirteen patients. The coefficient of variation (CV) of background fibroglandular tissue or fat tissue were measured to evaluate the performance of the noise reduction. The SUVmax and SUVpeak of lesions were also measured. The agreement of the SUV measurements was evaluated by Bland-Altman plots. Results: The CV of background fibroglandular tissue in the LC + DL images was significantly lower (9.10±2.76) than the CVs in the LC (13.60± 3.66) and LC + Gaussian images (11.51± 3.56). No significant difference was observed in both SUVmax and SUVpeak of lesions between LC + DL and reference images. For the visual assessment, the smoothness rating for the LC + DL images was significantly better than that for the other images except for the reference images. Conclusion: Our model reduced the noise in dbPET images acquired in about half the emission time while preserving quantitative values of lesions. This study demonstrates that machine learning is feasible and potentially performs better than conventional post-image filtering in dbPET denoising.

Diagnostic Utility of an Adjusted DWI Lexicon Using Multiple b-values to Evaluate Breast Lesions in Combination with BI-RADS.

PURPOSE: We aimed to investigate the diagnostic feasibility of an adjusted diffusion-weighted imaging (DWI) lexicon using multiple b values to assess breast lesions according to DWI-based breast imaging reporting and data system (BI-RADS). METHODS: This Institutional Review Board (IRB)-approved prospective study included 127 patients with suspected breast cancer. Breast MRI was performed using a 3T scanner. Breast DW images were acquired using five b-values of 0, 200, 800, 1000, and 1500 s/mm2 (5b-value DWI) on 3T MRI. Two readers independently assessed lesion characteristics and normal breast tissue using DWI alone (5b-value DWI and 2b-value DWI with b = 0 and 800 s/mm2) according to DWI-based BI-RADS and in combination with the standard dynamic contrast-enhanced images (combined MRI). Interobserver and intermethod agreements were assessed using kappa statistics. The specificity and sensitivity of lesion classification were evaluated. RESULTS: Ninety-five breast lesions (39 malignant and 56 benign) were evaluated. Interobserver agreement for lesion assessment on 5b-value DWI was very good (k ≥ 0.82) for DWI-based BI-RADS categories, lesion type, and mass characteristics; good (k = 0.75) in breast composition; and moderate (k ≥ 0.44) in background parenchymal signal (BPS) and non-mass distribution. Intermethod agreement between assessments performed using either 5b-value DWI or combined MRI was good-to-moderate (k = 0.52-0.67) for lesion type; moderate (k = 0.49-0.59) for DWI-based BI-RADS category and mass characteristics; and fair (k = 0.25-0.40) for mass shape, BPS, and breast composition. The sensitivity and positive predictive values (PPVs) for 5b-value DWI were 79.5%, 84.6% and 60.8%, 61.1% for each reader, respectively; 74.4%, 74.4% and 63.0%, 61.7% for 2b-value DWI; and 97.4%, 97.4% and 73.1%, 76.0% for combined MRI. The specificity and negative predictive values (NPVs) were 64.3%, 62.5% and 81.8%, 85.4% for 5b-value DWI; 69.6%, 67.9% and 79.6%, 79.2% for 2b-value DWI; and 75.0%, 78.6% and 97.7%, 97.8% for combined MRI. CONCLUSION: Good observer agreement was observed in the 5b-value DWI. The 5b-value DWI based on multiple b-values might have the potential to complement the 2b-value DWI; however, their diagnostic performance tended to be inferior to that of combined MRI for the characterization of breast tumors.

Deep Infantile Hemangioma in the Involuting Phase That Was Difficult to Diagnose before Surgery.

Infantile hemangioma (IH) is a common pediatric vascular tumor and is easily diagnosed in most cases based on the clinical course and appearance, but deep IHs are difficult to diagnose based on external appearance alone. Clinical and imaging findings are therefore important clues to the diagnosis of soft tissue tumors; however, a definitive diagnosis is decided based on the pathological examination of biopsy or resection specimens. A 1-year-old girl with a subcutaneous mass on her glabella was referred to our hospital. At 3 months of age, her mother noticed a tumor that swelled when she cried. It gradually enlarged, and ultrasonography and magnetic resonance imaging were performed at 12 months of age. Doppler ultrasonography showed a hypo-vascular mass. Magnetic resonance imaging revealed a subcutaneous mass with low-intensity on T1-weighted image and slightly high-intensity on T2-weighted image, with tiny flow voids. Computed tomography showed no frontal bone defect. The soft tissue tumor could not be diagnosed based on these imaging findings; thus, we decided to perform total resection under general anesthesia. A histopathological examination showed a highly cellular tumor with capillaries with opened small vascular channels and glucose transporter 1 positivity. Thus, it was diagnosed as deep IH transitioning from the proliferative phase to the involuting phase. Deep IHs are difficult to diagnose because characteristic imaging findings disappear during the involuting phase. We emphasize the importance of performing Doppler ultrasonography in the early phase (eg, at 6 months of age) for soft tissue tumors of infancy.

Diffusion kurtosis imaging as a biomarker of breast cancer.

Diffusion kurtosis imaging (DKI) is a diffusion-weighted imaging method that describes non-Gaussian signal behavior using a relatively simple mathematical model. A parameter, kurtosis K, describes the deviation of the diffusion signal decay from a Gaussian pattern. The deviation reflects the complexity of the tissue microstructure affecting water diffusion. Several studies have investigated the diagnostic performance of DKI in distinguishing malignant from benign breast lesions. DKI has been reported to correlate with subtypes and with several molecular and other factors related to the treatment and prognosis of breast cancer. Some technical considerations remain to be resolved for the clinical application of DKI in the breast. Advances in knowledge: DKI, which increases the sensitivity to complex tissue microstructure compared to standard DWI, has been applied in the breast, allowing to increase clinical performance in distinguishing malignant from benign lesions and in predicting prognosis or treatment response in breast cancer.