Structured illumination microscopy for cancer identification in diagnostic breast biopsies.

Breast cancer is the second most common cancer diagnosed in women in the US with almost 280,000 new cases anticipated in 2023. Currently, on-site pathology for location guidance is not available during the collection of breast biopsies or during surgical intervention procedures. This shortcoming contributes to repeat biopsy and re-excision procedures, increasing the cost and patient discomfort during the cancer management process. Both procedures could benefit from on-site feedback, but current clinical on-site evaluation techniques are not commonly used on breast tissue because they are destructive and inaccurate. Ex-vivo microscopy is an emerging field aimed at creating histology-analogous images from non- or minimally-processed tissues, and is a promising tool for addressing this pain point in clinical cancer management. We investigated the ability structured illumination microscopy (SIM) to generate images from freshly-obtained breast tissues for structure identification and cancer identification at a speed compatible with potential on-site clinical implementation. We imaged 47 biopsies from patients undergoing a guided breast biopsy procedure using a customized SIM system and a dual-color fluorescent hematoxylin & eosin (H&E) analog. These biopsies had an average size of 0.92 cm2 (minimum 0.1, maximum 4.2) and had an average imaging time of 7:29 (minimum 0:22, maximum 37:44). After imaging, breast biopsies were submitted for standard histopathological processing and review. A board-certified pathologist returned a binary diagnostic accuracy of 96% when compared to diagnoses from gold-standard histology slides, and key tissue features including stroma, vessels, ducts, and lobules were identified from the resulting images.

A meta-analysis of MRI radiomics-based diagnosis for BI-RADS 4 breast lesions.

OBJECTIVE: The aim of this study is to conduct a systematic evaluation of the diagnostic efficacy of Breast Imaging Reporting and Data System (BI-RADS) 4 benign and malignant breast lesions using magnetic resonance imaging (MRI) radiomics. METHODS: A systematic search identified relevant studies. Eligible studies were screened, assessed for quality, and analyzed for diagnostic accuracy. Subgroup and sensitivity analyses explored heterogeneity, while publication bias, clinical relevance and threshold effect were evaluated. RESULTS: This study analyzed a total of 11 studies involving 1,915 lesions in 1,893 patients with BI-RADS 4 classification. The results showed that the combined sensitivity and specificity of MRI radiomics for diagnosing BI-RADS 4 lesions were 0.88 (95% CI 0.83-0.92) and 0.79 (95% CI 0.72-0.84). The positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were 4.2 (95% CI 3.1-5.7), 0.15 (95% CI: 0.10-0.22), and 29.0 (95% CI 15-55). The summary receiver operating characteristic (SROC) analysis yielded an area under the curve (AUC) of 0.90 (95% CI 0.87-0.92), indicating good diagnostic performance. The study found no significant threshold effect or publication bias, and heterogeneity among studies was attributed to various factors like feature selection algorithm, radiomics algorithms, etc. Overall, the results suggest that MRI radiomics has the potential to improve the diagnostic accuracy of BI-RADS 4 lesions and enhance patient outcomes. CONCLUSION: MRI-based radiomics is highly effective in diagnosing BI-RADS 4 benign and malignant breast lesions, enabling improving patients' medical outcomes and quality of life.

Hyperspectral dark-field microscopy of human breast lumpectomy samples for tumor margin detection in breast-conserving surgery.

Significance: Hyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries. Aim: We expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples. Approach: Breast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the K-means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes. Results: The manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised K-means algorithm. The unsupervised K-means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas' unique endmembers produced by the two methods agree with each other within <2% residual error margin. Conclusions: Our report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas' unique endmembers used by the two methods agree to <2% residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or validate more advanced unsupervised data cube analysis algorithms, such as effective neural networks for efficient subtype classification.

A novel preclinical model of the normal human breast.

Improved screening and treatment have decreased breast cancer mortality, although incidence continues to rise. Women at increased risk of breast cancer can be offered risk reducing treatments, such as tamoxifen, but this has not been shown to reduce breast cancer mortality. New, more efficacious, risk-reducing agents are needed. The identification of novel candidates for prevention is hampered by a lack of good preclinical models. Current patient derived in vitro and in vivo models cannot fully recapitulate the complexities of the human tissue, lacking human extracellular matrix, stroma, and immune cells, all of which are known to influence therapy response. Here we describe a normal breast explant model utilising a tuneable hydrogel which maintains epithelial proliferation, hormone receptor expression, and residency of T cells and macrophages over 7 days. Unlike other organotypic tissue cultures which are often limited by hyper-proliferation, loss of hormone signalling, and short treatment windows (< 48h), our model shows that tissue remains viable over 7 days with none of these early changes. This offers a powerful and unique opportunity to model the normal breast and study changes in response to various risk factors, such as breast density and hormone exposure. Further validation of the model, using samples from patients undergoing preventive therapies, will hopefully confirm this to be a valuable tool, allowing us to test novel agents for breast cancer risk reduction preclinically.

Functional hemodynamic imaging markers for the prediction of pathological outcomes in breast cancer patients treated with neoadjuvant chemotherapy.

Significance: Achieving pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT) is a significant predictor of increased likelihood of survival in breast cancer patients. Early prediction of pCR is of high clinical value as it could allow personalized adjustment of treatment regimens in non-responding patients for improved outcomes. Aim: We aim to assess the association between hemoglobin-based functional imaging biomarkers derived from diffuse optical tomography (DOT) and the pathological outcome represented by pCR at different timepoints along the course of NACT. Approach: Twenty-two breast cancer patients undergoing NACT were enrolled in a multimodal DOT and X-ray digital breast tomosynthesis (DBT) imaging study in which their breasts were imaged at different compression levels. Logistic regressions were used to study the associations between DOT-derived imaging markers evaluated after the first and second cycles of chemotherapy, respectively, with pCR status determined after the conclusion of NACT at the time of surgery. Receiver operating characteristic curve analysis was also used to explore the predictive performance of selected DOT-derived markers. Results: Normalized tumor HbT under half compression was significantly lower in the pCR group compared to the non-pCR group after two chemotherapy cycles (p=0.042). In addition, the change in normalized tumor StO2 upon reducing compression from full to half mammographic force was identified as another potential indicator of pCR at an earlier time point, i.e., after the first chemo cycle (p=0.038). Exploratory predictive assessments showed that AUCs using DOT-derived functional imaging markers as predictors reach as high as 0.75 and 0.71, respectively, after the first and second chemo cycle, compared to AUCs of 0.50 and 0.53 using changes in tumor size measured on DBT and MRI. Conclusions: These findings suggest that breast DOT could be used to assist response assessment in women undergoing NACT, a critical but unmet clinical need, and potentially enable personalized adjustments of treatment regimens.

Improvement of Phased Antenna Array Applied in Focused Microwave Breast Hyperthermia.

Focused microwave breast hyperthermia (FMBH) employs a phased antenna array to perform beamforming that can focus microwave energy at targeted breast tumors. Selective heating of the tumor endows the hyperthermia treatment with high accuracy and low side effects. The effect of FMBH is highly dependent on the applied phased antenna array. This work investigates the effect of polarizations of antenna elements on the microwave-focusing results by simulations. We explore two kinds of antenna arrays with the same number of elements using different digital realistic human breast phantoms. The first array has all the elements' polarization in the vertical plane of the breast, while the second array has half of the elements' polarization in the vertical plane and the other half in the transverse plane, i.e., cross polarization. In total, 96 sets of different simulations are performed, and the results show that the second array leads to a better focusing effect in dense breasts than the first array. This work is very meaningful for the potential improvement of the antenna array for FMBH, which is of great significance for the future clinical applications of FMBH. The antenna array with cross polarization can also be applied in microwave imaging and sensing for biomedical applications.

Savi Scout Localization for Extrapelvic Endometriosis Resection.

BACKGROUND Endometriosis is a common cause of chronic pelvic pain among women globally. Pharmacological therapy for endometriosis includes non-steroidal anti-inflammatory drugs (NSAIDs) and hormonal contraceptives, while surgical therapy often involves either laparoscopic excision and ablation of endometriosis implants or open surgery. Surgical therapy is one of the mainstays of treatment especially for extrapelvic endometriomas. However, little guidance exists for the treatment of non-palpable or intermittently palpable lesions of this nature. CASE REPORT A 33-year-old woman with a previous cesarean section presented with complaints of intermittent discomfort in the area between her umbilicus and the surgical incision, for the previous 7 years, that worsened during her menstrual cycle. A 3×3-cm area of fullness was only intermittently palpable during various clinic visits, but was visualizable on computed tomography and magnetic resonance imaging. Given the lesion's varying palpability, a Savi Scout radar localization device was placed into the lesion pre-operatively to aid with surgical resection. The mass was excised, pathologic examination revealed endometrial tissue, and the patient had an uncomplicated postoperative course with resolution of her symptoms. CONCLUSIONS Surgical removal of extrapelvic endometrioma lesions can be made difficult by varying levels of palpability or localizability due to a patient's menstrual cycle. The Savi Scout, most commonly used in breast mass localization, is a useful tool in guiding surgical excision of non-palpable or intermittently palpable extrapelvic endometrioma lesions.

Paget's disease of the breast: Presentation, treatment, and outcomes in a modern cohort.

BACKGROUND: Surgical management for Paget's disease (PD) of the breast is controversial. This study aims to assess outcomes of PD patients based on procedure type and determine the reliability of imaging in estimating disease extent. METHODS: A retrospective review analyzed clinicopathologic data of PD patients between 2009 and 2022. Pre-operative imaging size (PIS) was compared to post-operative pathology size (PPS) looking at concordance. RESULTS: Thirty patients had PD, 21 underwent total mastectomy (TM) and 9 breast conserving surgery (BCS). Seventeen patients (56.7 â€‹%) had a final diagnosis of invasive cancer (14 â€‹TM, 3 BCS), with no local recurrences. Only 6/19 (31.6 â€‹%) patients with positive findings on ultrasound/mammogram had concordance between PIS and PPS. There were no breast/chest wall recurrences with a median follow up of 35.9 months. CONCLUSION: Ultrasound and mammogram had poor concordance with pathological size. BCS is feasible in select patients. MRI may help guide management.

Decorin (DCN) Downregulation Activates Breast Stromal Fibroblasts and Promotes Their Pro-Carcinogenic Effects through the IL-6/STAT3/AUF1 Signaling.

Decorin (DCN), a member of the small leucine-rich proteoglycan gene family, is secreted from stromal fibroblasts with non-cell-autonomous anti-breast-cancer effects. Therefore, in the present study, we sought to elucidate the function of decorin in breast stromal fibroblasts (BSFs). We first showed DCN downregulation in active cancer-associated fibroblasts (CAFs) compared to their adjacent tumor counterpart fibroblasts at both the mRNA and protein levels. Interestingly, breast cancer cells and the recombinant IL-6 protein, both known to activate fibroblasts in vitro, downregulated DCN in BSFs. Moreover, specific DCN knockdown in breast fibroblasts modulated the expression/secretion of several CAF biomarkers and cancer-promoting proteins (α-SMA, FAP- α, SDF-1 and IL-6) and enhanced the invasion/proliferation abilities of these cells through activation of the STAT3/AUF1 signaling. Furthermore, DCN-deficient fibroblasts promoted the epithelial-to-mesenchymal transition and stemness processes in BC cells in a paracrine manner, which increased their resistance to cisplatin. These DCN-deficient fibroblasts also enhanced angiogenesis and orthotopic tumor growth in mice in a paracrine manner. On the other hand, ectopic expression of DCN in CAFs suppressed their active features and their paracrine pro-carcinogenic effects. Together, the present findings indicate that endogenous DCN suppresses the pro-carcinogenic and pro-metastatic effects of breast stromal fibroblasts.

Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection.

BACKGROUND: Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography considering breast density, between radiologists and AI standalone detection among Korean women. METHODS: We retrospectively included 89,855 Korean women who underwent their initial screening digital mammography from 2009 to 2020. Breast cancer within 12 months of the screening mammography was the reference standard, according to the National Cancer Registry. Lunit software was used to determine the probability of malignancy scores, with a cutoff of 10% for breast cancer detection. The AI's performance was compared with that of the final Breast Imaging Reporting and Data System category, as recorded by breast radiologists. Breast density was classified into four categories (A-D) based on the radiologist and AI-based assessments. The performance metrics (cancer detection rate [CDR], sensitivity, specificity, positive predictive value [PPV], recall rate, and area under the receiver operating characteristic curve [AUC]) were compared across breast density categories. RESULTS: Mean participant age was 43.5 ± 8.7 years; 143 breast cancer cases were identified within 12 months. The CDRs (1.1/1000 examination) and sensitivity values showed no significant differences between radiologist and AI-based results (69.9% [95% confidence interval [CI], 61.7-77.3] vs. 67.1% [95% CI, 58.8-74.8]). However, the AI algorithm showed better specificity (93.0% [95% CI, 92.9-93.2] vs. 77.6% [95% CI, 61.7-77.9]), PPV (1.5% [95% CI, 1.2-1.9] vs. 0.5% [95% CI, 0.4-0.6]), recall rate (7.1% [95% CI, 6.9-7.2] vs. 22.5% [95% CI, 22.2-22.7]), and AUC values (0.8 [95% CI, 0.76-0.84] vs. 0.74 [95% CI, 0.7-0.78]) (all P < 0.05). Radiologist and AI-based results showed the best performance in the non-dense category; the CDR and sensitivity were higher for radiologists in the heterogeneously dense category (P = 0.059). However, the specificity, PPV, and recall rate consistently favored AI-based results across all categories, including the extremely dense category. CONCLUSIONS: AI-based software showed slightly lower sensitivity, although the difference was not statistically significant. However, it outperformed radiologists in recall rate, specificity, PPV, and AUC, with disparities most prominent in extremely dense breast tissue.

Serum protein profiling reveals an inflammation signature as a predictor of early breast cancer survival.

BACKGROUND: Breast cancers exhibit considerable heterogeneity in their biology, immunology, and prognosis. Currently, no validated, serum protein-based tools are available to evaluate the prognosis of patients with early breast cancer. METHODS: The study population consisted of 521 early-stage breast cancer patients with a median follow-up of 8.9 years. Additionally, 61 patients with breast fibroadenoma or atypical ductal hyperplasia were included as controls. We used a proximity extension assay to measure the preoperative serum levels of 92 proteins associated with inflammatory and immune response processes. The invasive cancers were randomly split into discovery (n = 413) and validation (n = 108) cohorts for the statistical analyses. RESULTS: Using LASSO regression, we identified a nine-protein signature (CCL8, CCL23, CCL28, CSCL10, S100A12, IL10, IL10RB, STAMPB2, and TNFß) that predicted various survival endpoints more accurately than traditional prognostic factors. In the time-dependent analyses, the prognostic power of the model remained rather stable over time. We also developed and validated a 17-protein model with the potential to differentiate benign breast lesions from malignant lesions (Wilcoxon p < 2.2*10- 16; AUC 0.94). CONCLUSIONS: Inflammation and immunity-related serum proteins have the potential to rise above the classical prognostic factors of early-stage breast cancer. They may also help to distinguish benign from malignant breast lesions.

[Non-primary solid malignancies of breast in needle core biopsy: a clinicopathological analysis of 23 cases].

Objective: To investigate the accurate diagnosis and differential diagnosis of non-primary solid malignant tumors in breast needle core biopsy. Methods: Twenty-three cases of breast, axilla or neck lymph nodes pathologically diagnosed as non-primary solid malignant tumors were collected at the First Affiliated Hospital of Nanjing Medical University, Nanjing, China from January 2013 to March 2023. The differential diagnoses and diagnostic features were analyzed, based on combining clinical data, histology, and expression characteristics of biomarkers. Results: All patients were female, with age ranging from 29 to 75 years (average 56 years). The average time from the diagnosis of primary tumor to the current diagnosis was 21 months (0 to 204 months).The primary sites included the ovary (9 cases), the lung (5 cases), the gastrointestinal tract (4 cases), the pancreas, intrahepatic bile duct, thyroid gland, nasal cavity and forearm skin (1 case each). No carcinoma in situ was found in any of the cases. The morphological differences were significant among the tumors, but similar to the primary tumors. The tumors of neuroendocrine and female reproductive tract had great morphological and immunophenotypic overlaps with breast cancer. Metastatic lung cancer cells showed obvious atypia and tumor giant cells. The morphology and immunophenotype of metastatic serous carcinoma of female reproductive system might resemble invasive micropapillary carcinoma of the breast. Metastatic adenocarcinoma of the gastrointestinal tract often had features of mucous secretion. Metastatic neuroendocrine tumors were bland in appearance and morphologically similar to solid papillary carcinoma of breast, but negative for ER. TRPS1 was mostly negative (18/23) and variably positive in ovarian (4/9) and intrahepatic bile duct (1/1) tumors. Conclusions: The diagnosis of breast needle core biopsy specimen should be combined with clinical history, imaging study, and careful examination of histological features, such as presence of in situ component, morphological similarity between the primary and metastatic tumors, and using appropriate markers to differentiate the primary from metastatic tumors.

Case report: Primary vulvar adenocarcinoma of mammary gland type-its genetic characteristics by focused next-generation sequencing.

Mammary-like vulvar adenocarcinoma (MLVA) is an exceedingly rare subtype of vulvar adenocarcinoma that shares features with mammary gland tissue. Due to its rarity and lack of consensus, MLVA presents diagnostic challenges to pathologists. We present the case of a 59-year-old female with an ulcerated mass on the right side of the external genitalia, diagnosed as MLVA. Comprehensive immunohistochemistry (IHC) and gene sequencing studies were performed to characterize the tumor. IHC analysis revealed triple expression of hormonal receptors (estrogen receptor, progesterone receptor, and HER2), supporting the mammary gland origin of the tumor. Gene sequencing identified unique genetic mutations associated with the expression of hormonal markers. One fusion gene (ERBB2-NAGLU) has not been reported in any tumors, and other mutations with unique mutation types have not been previously reported in MLVA. Our findings shed light on the molecular characteristics of MLV and may help improve the diagnosis and treatment of this rare type of vulvar adenocarcinoma.

Quantitative characterization of breast lesions and normal fibroglandular tissue using compartmentalized diffusion-weighted model: comparison of intravoxel incoherent motion and restriction spectrum imaging.

BACKGROUND: To compare the compartmentalized diffusion-weighted models, intravoxel incoherent motion (IVIM) and restriction spectrum imaging (RSI), in characterizing breast lesions and normal fibroglandular tissue. METHODS: This prospective study enrolled 152 patients with 157 histopathologically verified breast lesions (41 benign and 116 malignant). All patients underwent a full-protocol preoperative breast MRI, including a multi-b-value DWI sequence. The diffusion parameters derived from the mono-exponential model (ADC), IVIM model (Dt, Dp, f), and RSI model (C1, C2, C3, C1C2, F1, F2, F3, F1F2) were quantitatively measured and then compared among malignant lesions, benign lesions and normal fibroglandular tissues using Kruskal-Wallis test. The Mann-Whitney U-test was used for the pairwise comparisons. Diagnostic models were built by logistic regression analysis. The ROC analysis was performed using five-fold cross-validation and the mean AUC values were calculated and compared to evaluate the discriminative ability of each parameter or model. RESULTS: Almost all quantitative diffusion parameters showed significant differences in distinguishing malignant breast lesions from both benign lesions (other than C2) and normal fibroglandular tissue (all parameters) (all P < 0.0167). In terms of the comparisons of benign lesions and normal fibroglandular tissues, the parameters derived from IVIM (Dp, f) and RSI (C1, C2, C1C2, F1, F2, F3) showed significant differences (all P < 0.005). When using individual parameters, RSI-derived parameters-F1, C1C2, and C2 values yielded the highest AUCs for the comparisons of malignant vs. benign, malignant vs. normal tissue and benign vs. normal tissue (AUCs = 0.871, 0.982, and 0.863, respectively). Furthermore, the combined diagnostic model (IVIM + RSI) exhibited the highest diagnostic efficacy for the pairwise discriminations (AUCs = 0.893, 0.991, and 0.928, respectively). CONCLUSIONS: Quantitative parameters derived from the three-compartment RSI model have great promise as imaging indicators for the differential diagnosis of breast lesions compared with the bi-exponential IVIM model. Additionally, the combined model of IVIM and RSI achieves superior diagnostic performance in characterizing breast lesions.

Premastectomy Radiotherapy and Immediate Breast Reconstruction: A Randomized Clinical Trial.

Importance: Premastectomy radiotherapy (PreMRT) is a new treatment sequence to avoid the adverse effects of radiotherapy on the final breast reconstruction while achieving the benefits of immediate breast reconstruction (IMBR). Objective: To evaluate outcomes among patients who received PreMRT and regional nodal irradiation (RNI) followed by mastectomy and IMBR. Design, Setting, and Participants: This was a phase 2 single-center randomized clinical trial conducted between August 3, 2018, and August 2, 2022, evaluating the feasibility and safety of PreMRT and RNI (including internal mammary lymph nodes). Patients with cT0-T3, N0-N3b breast cancer and a recommendation for radiotherapy were eligible. Intervention: This trial evaluated outcomes after PreMRT followed by mastectomy and IMBR. Patients were randomized to receive either hypofractionated (40.05 Gy/15 fractions) or conventionally fractionated (50 Gy/25 fractions) RNI. Main Outcome and Measures: The primary outcome was reconstructive failure, defined as complete autologous flap loss. Demographic, treatment, and outcomes data were collected, and associations between multiple variables and outcomes were evaluated. Analysis was performed on an intent-to-treat basis. Results: Fifty patients were enrolled. Among 49 evaluable patients, the median age was 48 years (range, 31-72 years), and 46 patients (94%) received neoadjuvant systemic therapy. Twenty-five patients received 50 Gy in 25 fractions to the breast and 45 Gy in 25 fractions to regional nodes, and 24 patients received 40.05 Gy in 15 fractions to the breast and 37.5 Gy in 15 fractions to regional nodes, including internal mammary lymph nodes. Forty-eight patients underwent mastectomy with IMBR, at a median of 23 days (IQR, 20-28.5 days) after radiotherapy. Forty-one patients had microvascular autologous flap reconstruction, 5 underwent latissimus dorsi pedicled flap reconstruction, and 2 had tissue expander placement. There were no complete autologous flap losses, and 1 patient underwent tissue expander explantation. Eight of 48 patients (17%) had mastectomy skin flap necrosis of the treated breast, of whom 1 underwent reoperation. During follow-up (median, 29.7 months [range, 10.1-65.2 months]), there were no locoregional recurrences or distant metastasis. Conclusions and Relevance: This randomized clinical trial found PreMRT and RNI followed by mastectomy and microvascular autologous flap IMBR to be feasible and safe. Based on these results, a larger randomized clinical trial of hypofractionated vs conventionally fractionated PreMRT has been started (NCT05774678). Trial Registration: ClinicalTrials.gov Identifier: NCT02912312.

Breast Tomographic Ultrasound: The Spectrum from Current Dense Breast Cancer Screenings to Future Theranostic Treatments.

This review provides unique insights to the scientific scope and clinical visions of the inventors and pioneers of the SoftVue breast tomographic ultrasound (BTUS). Their >20-year collaboration produced extensive basic research and technology developments, culminating in SoftVue, which recently received the Food and Drug Administration's approval as an adjunct to breast cancer screening in women with dense breasts. SoftVue's multi-center trial confirmed the diagnostic goals of the tissue characterization and localization of quantitative acoustic tissue differences in 2D and 3D coronal image sequences. SoftVue mass characterizations are also reviewed within the standard cancer risk categories of the Breast Imaging Reporting and Data System. As a quantitative diagnostic modality, SoftVue can also function as a cost-effective platform for artificial intelligence-assisted breast cancer identification. Finally, SoftVue's quantitative acoustic maps facilitate noninvasive temperature monitoring and a unique form of time-reversed, focused US in a single theranostic device that actually focuses acoustic energy better within the highly scattering breast tissues, allowing for localized hyperthermia, drug delivery, and/or ablation. Women also prefer the comfort of SoftVue over mammograms and will continue to seek out less-invasive breast care, from diagnosis to treatment.

The Relationship between Breast Cancer Subtypes, Prognostic Factors, and Apparent Diffusion Coefficient Histogram Analysis.

BACKGROUND: Diffusion Magnetic Resonance Imaging (MRI) is a useful method to evaluate tumor biology and tumor microstructure. The apparent diffusion coefficient (ADC) value correlates negatively with the cellular density of the tumor. OBJECTIVE: This study aimed to investigate the effectiveness of the ADC histogram analysis in showing the relationship between breast cancer prognostic factors and ADC parameters. METHODS: This study is a retrospective observational descriptive study. ADC histogram parameters were evaluated in all tumor volumes of 67 breast cancer patients. Minimum, 5, 10, 25, 50, 75, 90, 95 percentiles, maximum, mean, median ADC values, kurtosis, and skewness were calculated. Breast MRI examinations were performed on a 3T MR scanner. We evaluated the fibroglandular tissue density of bilateral breasts, background enhancement, localization of masses, multifocality-multicentricity, shape, rim, internal contrast enhancement, and kinetic curve on breast MRI. BIRADS scoring was performed according to breast MRI. Pathologically, histologic type, histologic grade, HER 2, Ki 67, ER-, and PR status were evaluated. RESULTS: A significant correlation was found between tumor volume and ADC scores. There is a significant correlation between min ADC values (p< 0.031), max ADC (p< 0.001), and skewness (p< 0.019). A significant correlation was found between tumor kurtosis and lymph nodes (p< 0.029). There was a significant difference in ADC values depending on ER-and PRstatus. (for ER p = 0.004, p = 0.018, p = 0.010, p = 0.008, p = 0.004, p = 0.004, p = 0.02, p = 0.02 and p = 0.038, for PR p < 0.001, p = 0.028, p = 0.011, p = 0.001, p < 0.001, p =<0.001, p < 0.001, and p < 0.001, respectively; p < 0.05). These values were lower in ER-and PR-positive status than in ER-and PR-negative receptor status. According to HER2 status, there was a statistically significant difference in ADC

Diagnostic performance of contrast-enhanced mammography for suspicious findings in dense breasts: A systematic review and meta-analysis.

PURPOSE: Contrast-enhanced spectral imaging (CEM) is a new mammography technique, but its diagnostic value in dense breasts is still inconclusive. We did a systematic review and meta-analysis of studies evaluating the diagnostic performance of CEM for suspicious findings in dense breasts. MATERIALS AND METHODS: The PubMed, Embase, and Cochrane Library databases were searched systematically until August 6, 2023. Prospective and retrospective studies were included to evaluate the diagnostic performance of CEM for suspicious findings in dense breasts. The QUADAS-2 tool was used to evaluate the quality and risk of bias of the included studies. STATA V.16.0 and Review Manager V.5.3 were used to meta-analyze the included studies. RESULTS: A total of 10 studies (827 patients, 958 lesions) were included. These 10 studies reported the diagnostic performance of CEM for the workup of suspicious lesions in patients with dense breasts. The summary sensitivity and summary specificity were 0.95 (95% CI, 0.92-0.97) and 0.81 (95% CI, 0.70-0.89), respectively. Enhanced lesions, circumscribed margins, and malignancy were statistically correlated. The relative malignancy OR value of the enhanced lesions was 28.11 (95% CI, 6.84-115.48). The relative malignancy OR value of circumscribed margins was 0.17 (95% CI, 0.07-0.45). CONCLUSION: CEM has high diagnostic performance in the workup of suspicious findings in dense breasts, and when lesions are enhanced and have irregular margins, they are often malignant.

Study on the differential diagnosis of benign and malignant breast lesions using a deep learning model based on multimodal images.

OBJECTIVE: To establish a multimodal model for distinguishing benign and malignant breast lesions. MATERIALS AND METHODS: Clinical data, mammography, and MRI images (including T2WI, diffusion-weighted images (DWI), apparent diffusion coefficient (ADC), and DCE-MRI images) of 132 benign and breast cancer patients were analyzed retrospectively. The region of interest (ROI) in each image was marked and segmented using MATLAB software. The mammography, T2WI, DWI, ADC, and DCE-MRI models based on the ResNet34 network were trained. Using an integrated learning method, the five models were used as a basic model, and voting methods were used to construct a multimodal model. The dataset was divided into a training set and a prediction set. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the model were calculated. The diagnostic efficacy of each model was analyzed using a receiver operating characteristic curve (ROC) and an area under the curve (AUC). The diagnostic value was determined by the DeLong test with statistically significant differences set at P < 0.05. RESULTS: We evaluated the ability of the model to classify benign and malignant tumors using the test set. The AUC values of the multimodal model, mammography model, T2WI model, DWI model, ADC model and DCE-MRI model were 0.943, 0.645, 0.595, 0.905, 0.900, and 0.865, respectively. The diagnostic ability of the multimodal model was significantly higher compared with that of the mammography and T2WI models. However, compared with the DWI, ADC, and DCE-MRI models, there was no significant difference in the diagnostic ability of these models. CONCLUSION: Our deep learning model based on multimodal image training has practical value for the diagnosis of benign and malignant breast lesions.

Non-mass-type ductal carcinoma in situ of the breast on ultrasound: Features and pathological analysis.

AIMS: The aims of this study were to investigate the ultrasound features of non-mass-type ductal carcinoma in situ (DCIS) of the breast and conduct a pathological analysis. MATERIAL AND METHODS: Ultrasound images of 32 cases of non-mass-type DCIS of the breast, collected between September 2014 and June 2016, were analyzed. The characteristics of the lesions, including border, internal echogenicity, local glandular hyperplasia, micro-calcification, and intra-tumoral blood flow resistance index (RI), were analyzed, and a concurrent pathological analysis was conducted. RESULTS: Obvious local glandular hyperplasia was commonly observed in the 32 cases of non-mass-type DCIS of the breast. The internal echogenicity varied in intensity, exhibiting a "leopard pattern" or "zebra pattern." Color Doppler imaging revealed abundant blood flow signals within the lesion with an RI of >0.7. Isolated duct dilatation and micro-calcifications were occasionally observed within the lesions. High-grade DCIS was the predominant pathological type of non-mass-type DCIS. CONCLUSIONS: Non-mass-type DCIS of the breast often presents with obvious local glandular hyperplasia and varying internal echogenicity. High-grade DCIS is the frequent pathological type. Color Doppler imaging and RI measurement can assist in diagnosing non-mass-type DCIS of the breast.