Neoadjuvant pegylated liposomal doxorubicin- and epirubicin-based combination therapy regimens for early breast cancer: a multicenter retrospective case-control study.

PURPOSE: This study aimed to compare the effectiveness and safety of pegylated liposomal doxorubicin (PLD)-based and epirubicin-based combination therapy regimen as neoadjuvant therapy for early breast cancer. METHODS: Patients with stage I-III breast cancer who underwent neoadjuvant therapy followed by surgery between January 2018 and December 2019 were retrospectively reviewed. The primary outcome was pathological complete response (pCR) rate. The secondary outcome was radiologic complete response (rCR) rate. Outcomes were compared between treatment groups PLD-cyclophosphamide followed by docetaxel (LC-T group) or epirubicin-cyclophosphamide followed by docetaxel (EC-T group), using both propensity-score matched (matched) and unmatched data. RESULTS: Data were analyzed from patients who received neoadjuvant LC-T (n = 178) or EC-T (n = 181) treatment. The overall pCR rate and rCR rate were higher in the LC-T group compared with the EC-T group (unmatched pCR: 25.3% vs. 15.5%, p = 0.026; rCR: 14.7% vs. 6.7%, p = 0.016; matched pCR: 26.9% vs. 16.1%, p = 0.034; rCR: 15.5% vs. 7.4%, p = 0.044). Analysis by molecular subtype showed that compared with EC-T treatment, LC-T treatment achieved significantly greater pCR rate in triple-negative subtype and greater rCR rate in Her2 (+) subtype. CONCLUSIONS: Neoadjuvant PLD-based therapy may be a potential option for patients with early-stage breast cancer. The current results warrant further investigation.

Development of U-Net Breast Density Segmentation Method for Fat-Sat MR Images Using Transfer Learning Based on Non-Fat-Sat Model.

To develop a U-net deep learning method for breast tissue segmentation on fat-sat T1-weighted (T1W) MRI using transfer learning (TL) from a model developed for non-fat-sat images. The training dataset (N = 126) was imaged on a 1.5 T MR scanner, and the independent testing dataset (N = 40) was imaged on a 3 T scanner, both using fat-sat T1W pulse sequence. Pre-contrast images acquired in the dynamic-contrast-enhanced (DCE) MRI sequence were used for analysis. All patients had unilateral cancer, and the segmentation was performed using the contralateral normal breast. The ground truth of breast and fibroglandular tissue (FGT) segmentation was generated using a template-based segmentation method with a clustering algorithm. The deep learning segmentation was performed using U-net models trained with and without TL, by using initial values of trainable parameters taken from the previous model for non-fat-sat images. The ground truth of each case was used to evaluate the segmentation performance of the U-net models by calculating the dice similarity coefficient (DSC) and the overall accuracy based on all pixels. Pearson's correlation was used to evaluate the correlation of breast volume and FGT volume between the U-net prediction output and the ground truth. In the training dataset, the evaluation was performed using tenfold cross-validation, and the mean DSC with and without TL was 0.97 vs. 0.95 for breast and 0.86 vs. 0.80 for FGT. When the final model developed with and without TL from the training dataset was applied to the testing dataset, the mean DSC was 0.89 vs. 0.83 for breast and 0.81 vs. 0.81 for FGT, respectively. Application of TL not only improved the DSC, but also decreased the required training case number. Lastly, there was a high correlation (R2 > 0.90) for both the training and testing datasets between the U-net prediction output and ground truth for breast volume and FGT volume. U-net can be applied to perform breast tissue segmentation on fat-sat images, and TL is an efficient strategy to develop a specific model for each different dataset.

Genomewide copy number analysis of Müllerian adenosarcoma identified chromosomal instability in the aggressive subgroup.

Müllerian adenosarcomas are malignant gynecologic neoplasms. Advanced staging and sarcomatous overgrowth predict poor prognosis. Because the genomic landscape remains poorly understood, we conducted this study to characterize the genomewide copy number variations in adenosarcomas. Sixteen tumors, including eight with and eight without sarcomatous overgrowth, were subjected to a molecular inversion probe array analysis. Copy number variations, particularly losses, were significantly higher in cases with sarcomatous overgrowth. Frequent gains of chromosomal 12q were noted, often involving cancer-associated genes CDK4 (six cases), MDM2, CPM, YEATS4, DDIT3, GLI1 (five each), HMGA2 and STAT6 (four), without association with sarcomatous overgrowth status. The most frequent losses involved chromosomes 13q (five cases), 9p, 16q and 17q (four cases each) and were almost limited to cases with sarcomatous overgrowth. MDM2 and CDK4 amplification, as well as losses of RB1 (observed in two cases) and CDKN2A/B (one case), was verified by FISH. By immunohistochemistry, all MDM2/CDK4-coamplified cases were confirmed to overexpress both encoded proteins, whereas all four cases with (plus an additional four without) gain of HMGA2 overexpressed the HMGA2 protein. Both cases with RB1 loss were negative for the immunostaining of the encoded protein. Chromothripsis-like copy number profiles involving chromosome 12 or 14 were observed in three fatal cases, all of which harbored sarcomatous overgrowth. With whole chromosome painting and deconvolution fluorescent microscopy, dividing tumor cells in all three cases were shown to have scattered extrachromosomal materials derived from chromosomes involved by chromothripsis, suggesting that this phenomenon may serve as visual evidence for chromothripsis in paraffin tissue. In conclusion, we identified frequent chromosome 12q amplifications, including loci containing potential pharmacological targets. Global chromosomal instability and chromothripsis were more frequent in cases with sarcomatous overgrowth. To our knowledge, this is the first time that evidence of chromothripsis has been demonstrated in paraffin-embedded clinical tissues and in adenosarcomas.

Association of interleg difference of ankle brachial index with overall and cardiovascular mortality in chronic hemodialysis patients.

BACKGROUND: The ankle-brachial index (ABI) is associated with peripheral vascular atherosclerosis, adverse cardiovascular outcomes, and all-cause mortality. However, there were limited data available on studying the effect of interleg ABI difference. METHODS: We investigated the association of the interleg ABI difference with overall and cardiovascular mortality in chronic hemodialysis in a retrospective observational cohort of 369 Taiwanese patients undergoing chronic hemodialysis. RESULTS: An interleg ABI difference of ≥0.15 in hemodialysis patients had significant predictive power for all-cause and cardiovascular mortality in crude analysis. The hazard ratio (HR) for all-cause mortality was 3.00 [95% confidence interval (CI), 1.91-4.71]; the HR for cardiovascular mortality was 3.13 (95% CI, 1.82-5.38). After adjustment for confounding variables, this difference continued to have significant predictive power for all-cause mortality but lost its predictive power for fatal cardiac outcome. ABI <0.9 and high brachial-ankle pulse wave velocity were independently associated with an interleg ABI difference of ≥0.15 in hemodialysis patients. Moreover, in the subgroup analysis, we found that this difference was an independent factor for overall and cardiovascular mortality, particularly in elder patients, female patients, or those with ABI <0.9. CONCLUSION: Detection of an interleg ABI difference of ≥0.15 was an independent risk factor for overall mortality in hemodialysis patients but it may affect cardiovascular mortality through the effect of peripheral vascular disease.

Preoperative systemic inflammation response index: Clinicopathologic predictor of pathological complete response in HER2-positive breast cancer patients receiving neoadjuvant systemic therapy.

BACKGROUND: Multiple pretreatment systemic inflammatory markers (SIMs) have been reported as predictors of pathological complete response (pCR) after neoadjuvant systemic therapy (NST) in patients with breast cancer (BC). However, the most significant SIM remains to be conclusively identified, and variations among different molecular subtypes remain unknown. The objective of the study was to identify the most significant SIM in patients with human epidermal growth factor receptor 2 (HER2) positive BC, to construct a pCR-predictive nomogram combining it with other clinicopathologic factors, and to evaluate its prognostic value on survival. METHODS: We retrospectively reviewed the findings for 240 patients with stage I-III HER2-positive BC who underwent NST and subsequent surgery at Kaohsiung and Taichung Veterans General Hospital from 2011 to 2021. Clinicopathologic factors were analyzed by stepwise logistic regression with backward selection. The data were used to construct a nomogram plot for determining the pCR probability. Kaplan-Meier curves and log-rank test were used to evaluate disease-free survival (DFS) and overall survival (OS). RESULTS: Among the pretreatment SIMs, only the systemic inflammation response index (SIRI) was significantly related to pCR, with an optimal cutoff value of 1.27 × 10 9 /L. Stepwise logistic analyses indicated that clinical N stage, HER2 immunohistochemistry score, hormone receptor status, targeted therapy regimen, and SIRI were independent predictors of pCR, with an area under the curve of 0.722. The Hosmer-Lemeshow test and calibration curve revealed that the predictive ability was a good fit to actual observations. A nomogram was constructed based on the logistic model. The external validation of the model also revealed satisfactory discrimination and calibration. Kaplan-Meier analysis showed that patients with SIRI <1.27 had longer DFS and OS. CONCLUSION: Pretreatment SIRI <1.27 is predictive of pCR, DFS, and OS in HER2-positive BC. Our nomogram could efficiently predict pCR and facilitate clinical decision-making before neoadjuvant treatment.

Epigallocatechin-3-gallate combined with alpha lipoic acid attenuates high glucose-induced receptor for advanced glycation end products (RAGE) expression in human embryonic kidney cells.

The anti-oxidant effects of epigallocatechin gallate (EGCG) and alpha lipoic acid (ALA) have been demonstrated in previous studies. The kidney protection effects of EGCG and ALA in patients with kidney injury are still under investigation. The purpose of this study is to investigate the anti-inflammatory and anti-oxidant effects of EGCG and ALA on high glucose-induced human kidney cell damage. EGCG inhibited high glucose(HG)-induced TNF-α and IL-6 production in human embryonic kidney (HEK) cells. Both EGCG and ALA decreased HG-induced receptor of advanced glycation end products (RAGE) mRNA and protein expressions in HEK cells. EGCG and ALA also recovered HG-inhibited superoxide dismutase production and decreased ROS expressions in HEK cells. The synergism of EGCG and ALA was also studied. The effect of EGCG combined with ALA is greater than the effect of EGCG alone in all anti-inflammation and anti-oxidant experiments. Our studies provide a potential therapeutic application of EGCG and ALA in preventing progression of diabetic nephropathy.

Comparison of Quality of Life Between Breast Cancer Patients Treated With and Without Adjunctive Traditional Chinese Medicine in Taiwan.

In Taiwan, breast cancer has the highest incidence among all cancers. Although adjunctive traditional Chinese medicine treatment (TCM) have been used to ameliorate the side effects or discomfort caused by cancer treatments, no study has focused on the assessment of the quality of life of patients undergoing adjunctive TCM treatments. This study compared the quality of life between breast cancer patients treated with and without adjunctive TCM. Questionnaires were collected from 7 hospitals with a Chinese medicine clinic in 2018 to 2019. Breast cancer patients who had cancer stages I, II, or III and also underwent resection surgery were included in the study. They were divided into 2 groups: patients receiving cancer treatments with adjunctive traditional Chinese medicine (TCM group) and those receiving cancer treatments without adjunctive traditional Chinese medicine (non-TCM group). A 1:1 matching was used to obtain the study participants. The EQ-5D questionnaire was used to assess the quality of life. Statistical analysis was performed using the t-test and ANOVA to compare the differences between variables. The conditional multiple regression model was applied to explore the factors associated with quality of life in breast cancer patients. A total of 543 participants were surveyed, and 450 participants were included in the study. The EQ-5D score of the TCM group (81.60 ± 11.67) was significantly higher than that of the non-TCM group (78.80 ± 13.10; P < .05). The results of a conditional multiple regression model showed that the TCM group had a higher (3.45 points) quality of life than non-TCM group (P = .002) after adjusting for other related factors. After stratifying by cancer stage, patients with cancer stages II and III scored 5.58 and 4.35 points higher in the TCM group than did those in the non-TCM group (P < .05). Breast cancer patients undergoing cancer treatment with adjunctive traditional Chinese medicine have a higher quality of life than those treated without adjunctive traditional Chinese medicine.

A case report of secondary synchronous diagnosis of multiple myeloma and systemic lupus erythematosus after breast cancer treatment: A CARE-compliant article.

INTRODUCTION: Breast cancer (BC) is the most diagnosed cancer worldwide. Multiple myeloma (MM) is a hematologic malignancy characterized by the overproduction of monoclonal antibodies in the bone marrow. Systemic lupus erythematosus (SLE) is distinguished by the aberrant activity of the immune system with heterogeneous clinical manifestations. The coexistence of more than one major illness in a patient can present a diagnostic challenge for clinical physicians, especially when the comorbid diseases share a similar clinical presentation. Herein, we report an unusual case of secondary synchronous diagnosis of MM and SLE after BC treatment. PATIENT CONCERNS: A 69-year-old female patient with breast cancer experienced severe skin itching and rashes on the face, anterior chest wall, back, and trunk for two days before admission. She had high levels of immunoglobulin and anti-nuclear antibodies; low levels of complements 3 and 4; positive anti-cardiolipin-IgM, anti-beta 2 glycoprotein-1 (anti-ß2GP1) antibodies, and lupus anticoagulant results at serological testing. DIAGNOSIS: The postoperative pathology report showed ductal carcinoma in situ in the right breast. SLE was confirmed based on the 2019 European League Against Rheumatism/American College of Rheumatology (EULAR/ACR) criteria. IgG-κ type multiple myeloma was confirmed by bone marrow biopsy, and the patient was synchronously diagnosed with SLE and MM after BC treatment. INTERVENTIONS: Glucocorticoids and immunosuppressive agents, including intravenous hydrocortisone (5 g every 8 hours) and oral hydroxychloroquine (Plaquenil) (200 mg twice daily) were administered to treat SLE. One capsule of thalidomide 50 mg was administered orally every night at bedtime for MM. OUTCOMES: The patient died two days later, shortly after the administration of drugs, due to multiple organ failures secondary to pneumonia and respiratory failure. CONCLUSION: This is a case of MM and SLE after BC treatment. The present challenge was the early detection and accurate diagnosis of the secondary major illnesses, as the clinical manifestations were similar and non-specific between these two diseases. Awareness and prompt recognition of the common clinical symptoms of SLE and MM should be considered by clinical physicians to avoid delayed diagnoses and facilitate early treatment for a better prognosis.

Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non-Fat-Sat Images and Tested on Fat-Sat Images.

RATIONALE AND OBJECTIVES: Computer-aided methods have been widely applied to diagnose lesions on breast magnetic resonance imaging (MRI). The first step was to identify abnormal areas. A deep learning Mask Regional Convolutional Neural Network (R-CNN) was implemented to search the entire set of images and detect suspicious lesions. MATERIALS AND METHODS: Two DCE-MRI datasets were used, 241 patients acquired using non-fat-sat sequence for training, and 98 patients acquired using fat-sat sequence for testing. All patients have confirmed unilateral mass cancers. The tumor was segmented using fuzzy c-means clustering algorithm to serve as the ground truth. Mask R-CNN was implemented with ResNet-101 as the backbone. The neural network output the bounding boxes and the segmented tumor for evaluation using the Dice Similarity Coefficient (DSC). The detection performance, and the trade-off between sensitivity and specificity, was analyzed using free response receiver operating characteristic. RESULTS: When the precontrast and subtraction image of both breasts were used as input, the false positive from the heart and normal parenchymal enhancements could be minimized. The training set had 1469 positive slices (containing lesion) and 9135 negative slices. In 10-fold cross-validation, the mean accuracy = 0.86 and DSC = 0.82. The testing dataset had 1568 positive and 7264 negative slices, with accuracy = 0.75 and DSC = 0.79. When the obtained per-slice results were combined, 240 of 241 (99.5%) lesions in the training and 98 of 98 (100%) lesions in the testing datasets were identified. CONCLUSION: Deep learning using Mask R-CNN provided a feasible method to search breast MRI, localize, and segment lesions. This may be integrated with other artificial intelligence algorithms to develop a fully automatic breast MRI diagnostic system.

Prognostic and predictive factors of eribulin in patients with heavily pre-treated metastatic breast cancer.

ABSTRACT: A predictive marker for efficacy of eribulin administered as different lines of treatment in metastatic breast cancer (MBC) has not been identified. We aimed to determine the predictive factors for efficacy of eribulin administered as different lines of treatment in MBC patients.This restrospective cohort study included 49 heavily pre-treated MBC patients who received either eribulin monotherapy or combination therapy with eribulin and anti-Her2 therapy. Associations between clinical response of eribulin-based treatment, time-to-treatment failure (TTF), and possible predictive markers were investigated.Patients' median age was 55 years; 65% were ER+; 43% were HER2+; and 16% were triple-negative. Median TTF was 5.23 months and longer in non-visceral metastases patients. Eastern Cooperative Oncology Group (ECOG) status was 0-1; eribulin as ≥2nd-line treatment; eribulin combined with dual blockades; lymphocyte-monocyte ratio (LMR) ≥3; and monocyte-lymphocyte ratio (MLR) <0.4. In patients with eribulin as >3rd-line treatment, univariate analysis showed that ECOG status was 0-1, and LMR ≥3 and MLR <0.4 were associated with a low risk of TTF. Multivariate analysis showed that ECOG status 0-1 was an independent protective factor. Leukopenia and neutropenia were the most common manageable adverse events.ECOG status is an independent predictor for TTF, while LMR and MLR may have an interactive effect with other biomarkers (e.g., ECOG status) to predict response in MBC patients receiving eribulin as ≥2nd-line treatment.

Quantitative Measurement of Breast Tumors Using Intravoxel Incoherent Motion (IVIM) MR Images.

Breast magnetic resonance imaging (MRI) is currently a widely used clinical examination tool. Recently, MR diffusion-related technologies, such as intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI), have been extensively studied by breast cancer researchers and gradually adopted in clinical practice. In this study, we explored automatic tumor detection by IVIM-DWI. We considered the acquired IVIM-DWI data as a hyperspectral image cube and used a well-known hyperspectral subpixel target detection technique: constrained energy minimization (CEM). Two extended CEM methods-kernel CEM (K-CEM) and iterative CEM (I-CEM)-were employed to detect breast tumors. The K-means and fuzzy C-means clustering algorithms were also evaluated. The quantitative measurement results were compared to dynamic contrast-enhanced T1-MR imaging as ground truth. All four methods were successful in detecting tumors for all the patients studied. The clustering methods were found to be faster, but the CEM methods demonstrated better performance according to both the Dice and Jaccard metrics. These unsupervised tumor detection methods have the advantage of potentially eliminating operator variability. The quantitative results can be measured by using ADC, signal attenuation slope, D*, D, and PF parameters to classify tumors of mass, non-mass, cyst, and fibroadenoma types.

Adjuvant whole breast radiotherapy with simultaneous integrated boost to tumor bed with intensity modulated radiotherapy technique in elderly breast cancer patients.

BACKGROUND: Adjuvant whole breast radiotherapy is the standard of care for breast cancer patients after partial mastectomy. Intensity-modulated radiation therapy (IMRT) has been reported to reduce acute toxicities compared to conventional radiotherapy. IMRT with simultaneous integrated boost (SIB) technique can deliver higher doses to tumor bed and irradiate whole breast with a lower dose level to shorten overall treatment duration. This study presents the long-term results of adjuvant IMRT with SIB in elderly breast cancer patients who received partial mastectomy. METHODS: From January 2007 to January 2018, 93 elder breast cancer patients (≥65-year-old) who received IMRT with SIB technique after partial mastectomy were reviewed retrospectively. The axillary areas were managed with either sentinel lymph node biopsies or axillary lymph node dissection. The dose to whole breast was 50.4 Gy in 28 fractions in all patients and the dose to tumor bed was 61.6 to 66.4 Gy in 28 fractions. The primary end point is locoregional control. Secondary end points include: overall survival, breast cancer-specific survival, distant-metastases-free survival, disease-free survival, and acute and chronic toxicities. RESULTS: The median follow-up was 56.1 months. One patient had ipsilateral breast tumor recurrence, 3 patients had regional lymph node recurrence, and 9 patients had distant metastases. Death occurred in 5 patients, including 3 patients died of breast cancer progression. Five-year overall survival is 96.3% and 5-year locoregional recurrence-free survival is 96.4%. The 5-year breast cancer specific survival and 5-year distant metastases-free survival is 97.5% and 87.2%, respectively. Seven patients developed second primary cancer after RT. Eighty-one point seven percent patients had acute grade 1 dermatitis while 18.3% suffered from grade 2 dermatitis. The incidence of grade 1 pneumonitis and grade 1 stomatitis was 4.3% and 8.6%, respectively. CONCLUSIONS: Adjuvant IMRT with SIB technique is a safe and effective treatment strategy for elderly breast cancer patients after partial mastectomy.

Breast Tumor Detection and Classification Using Intravoxel Incoherent Motion Hyperspectral Imaging Techniques.

Breast cancer is a main cause of disease and death for women globally. Because of the limitations of traditional mammography and ultrasonography, magnetic resonance imaging (MRI) has gradually become an important radiological method for breast cancer assessment over the past decades. MRI is free of the problems related to radiation exposure and provides excellent image resolution and contrast. However, a disadvantage is the injection of contrast agent, which is toxic for some patients (such as patients with chronic renal disease or pregnant and lactating women). Recent findings of gadolinium deposits in the brain are also a concern. To address these issues, this paper develops an intravoxel incoherent motion- (IVIM-) MRI-based histogram analysis approach, which takes advantage of several hyperspectral techniques, such as the band expansion process (BEP), to expand a multispectral image to hyperspectral images and create an automatic target generation process (ATGP). After automatically finding suspected targets, further detection was attained by using kernel constrained energy minimization (KCEM). A decision tree and histogram analysis were applied to classify breast tissue via quantitative analysis for detected lesions, which were used to distinguish between three categories of breast tissue: malignant tumors (i.e., central and peripheral zone), cysts, and normal breast tissues. The experimental results demonstrated that the proposed IVIM-MRI-based histogram analysis approach can effectively differentiate between these three breast tissue types.