With decreasing mortality, the quality of life, spiritual needs, and mental health of breast cancer patients have become increasingly important. Demoralization is a poor prognostic factor for cancer patients. The extent of demoralization in breast cancer patients and its association with these factors remains unclear. This cross-sectional study was conducted at a Taiwanese medical center. We enrolled 121 participants (34 with high demoralization and 87 with low demoralization, as per the Mandarin Version of Demoralization Scale). High demoralization was associated with reduced quality of life, sleep quality, and spiritual interests. Multivariate analyses revealed that the scores of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire ≥ 62.5 (OR = 0.21, p = 0.002) and Spiritual Interests Related to Illness Tool Chinese Version ≥ 3.66 (OR = 0.11, p < 0.001) were associated with low demoralization. Demoralized patients with depression had a poorer quality of life and sleep quality. Although not statistically significant, depressed and demoralized participants were at a higher risk of suicide. Cancer patients with both depression and demoralization had the worst prognosis. Breast cancer patients exhibited demoralization when they had unmet bio-psycho-social-spiritual needs. An early assessment of demoralization may improve holistic healthcare for breast cancer patients.

(1) Background: Neoadjuvant therapy is widely used to treat locally advanced breast cancer. It has been recently shown that it can also improve the prognosis of patients during the early stages of breast cancer. In the past, advanced breast cancer with positive Human Epidermal growth factor Receptor 2 (HER2+) resulted in poor prognoses; however, outcomes have since changed after the introduction of HER2-targeting therapy. Achieving pathological Complete Response (pCR) is the most important aim, as it is a predictor of long-term outcomes in high-risk breast cancer subtypes. (2) Methods: We performed a retrospective review of all breast cancer patients who were treated with neoadjuvant therapy at Taichung Veterans General Hospital (VGHTC) between 2010 and 2018. A total of 147 HER2+ breast cancer patients who underwent neoadjuvant chemotherapy involving anthracycline and taxane-based regimens were enrolled. Within that population, 95 and 52 cases received single-blockade (Trastuzumab) and dual-blockade (Trastuzumab and Pertuzumab) neoadjuvant anti-HER2 therapy, respectively. (3) Results: The dual-blockade therapy group displayed a significantly higher pCR rate after surgery as compared to the single-blockade group (63.5% vs. 43.2%, p = 0.019). Advanced stage, larger tumor size, lymph node involvement and HER2 expression status were associated with the pCR rate. The 4-year OS was 85.2% and 100% in the single-blockage and dual-blockade therapy groups, respectively (p = 0.041). (4) Conclusion: Anthracycline, followed by taxane-based neoadjuvant chemotherapy combined with the dual HER2-blockade, had a higher pCR rate and better outcome when compared with the single HER2-blockade strategy in locally advanced HER2 breast cancer.

Breast cancer treatments often have negative effects on fertility, which pose challenges among patients who want to be parents in the future. This study aimed to examine the efficacy of oocyte cryopreservation, embryo cryopreservation, and ovarian tissue cryopreservation in patients with breast cancer. This retrospective review evaluated 42 patients with breast cancer who underwent fertility preservation at our center from January 2012 to December 2022. This review encompassed the demographic characteristics of the patients, cancer stages, treatment details, and types of fertility preservation procedures and their outcomes. The average age at disease diagnosis was 33.4 years. Approximately 90.4% of patients presented with early-stage cancer (≤2). Of 42 patients, 26 underwent oocyte cryopreservation; 17, embryo cryopreservation; and 2, ovarian tissue cryopreservation. Further, three patients received mixed treatment. The overall live birth rate was 63.2%. There are more live births in embryo cryopreservation group. The successful pregnancy group was significantly younger and had a remarkably higher quantity of preserved oocytes/embryos than the nonsuccessful pregnancy group. The oocyte and embryo utilization rates in cryopreservation were 7.69% and 52.94%, respectively. These findings underscored the importance of prompt, informed discussions about fertility preservation options. Fertility preservation in patients with breast cancer have promising reproductive outcomes, with embryo cryopreservation being particularly effective. Prompt counseling and individualized fertility preservation strategies are important for improving the likelihood of posttreatment pregnancy. Nevertheless, future research on the long-term psychological and emotional effects of different fertility preservation methods must be performed.

Drug evaluation in tumoroids lacks circulation, limiting infiltration analysis and real-time observation. Using tumor-microenvironment-on-chip (TMoC), this study confirms chemotherapy efficacy in mouse and patient tumors.

Background/Objectives: This research presents a novel analytical method for breast tumor characterization and tissue classification by leveraging intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) combined with hyperspectral imaging techniques and deep learning. Traditionally, dynamic contrast-enhanced MRI (DCE-MRI) is employed for breast tumor diagnosis, but it involves gadolinium-based contrast agents, which carry potential health risks. IVIM imaging extends conventional diffusion-weighted imaging (DWI) by explicitly separating the signal decay into components representing true molecular diffusion ($D$) and microcirculation of capillary blood (pseudo-diffusion or $D*$). This separation allows for a more comprehensive, non-invasive assessment of tissue characteristics without the need for contrast agents, thereby offering a safer alternative for breast cancer diagnosis. The primary purpose of this study was to evaluate different methods for breast tumor characterization using IVIM-DWI data treated as hyperspectral image stacks. Dice similarity coefficients and Jaccard indices were specifically used to evaluate the spatial segmentation accuracy of tumor boundaries, confirmed by experienced physicians on dynamic contrast-enhanced MRI (DCE-MRI), emphasizing detailed tumor characterization rather than binary diagnosis of cancer. Methods: The data source for this study consisted of breast MRI scans obtained from 22 patients diagnosed with mass-type breast cancer, resulting in 22 distinct mass tumor cases analyzed. MR images were acquired using a 3T MRI system (Discovery MR750 3.0 Tesla, GE Healthcare, Chicago, IL, USA) with axial IVIM sequences and a bipolar pulsed gradient spin echo sequence. Multiple b-values ranging from 0 to 2500 s/mm² were utilized, specifically thirteen original b-values (0, 15, 30, 45, 60, 100, 200, 400, 600, 1000, 1500, 2000, and 2500 s/mm²), with the last four b-value images replicated once for a total of 17 bands used in the analysis. The methodology involved several steps: acquisition of multi-b-value IVIM-DWI images, image pre-processing, including correction for motion and intensity inhomogeneity, treating the multi-b-value data as hyperspectral image stacks, applying hyperspectral techniques like band expansion, and evaluating three tumor detection methods: kernel-based constrained energy minimization (KCEM), iterative KCEM (I-KCEM), and deep neural networks (DNNs). The comparisons were assessed by evaluating the similarity of the detection results from each method to ground truth tumor areas, which were manually drawn on DCE-MRI images and confirmed by experienced physicians. Similarity was quantitatively measured using the Dice similarity coefficient and the Jaccard index. Additionally, the performance of the detectors was evaluated using 3D-ROC analysis and its derived criteria (AUC_OD, AUC_TD, AUC_BS, AUC_TD−BS, AUC_ODP, AUC_SNPR). Results: The findings objectively demonstrated that the DNN method achieved superior performance in breast tumor detection compared to KCEM and I-KCEM. Specifically, the DNN yielded a Dice similarity coefficient of 86.56% and a Jaccard index of 76.30%, whereas KCEM achieved 78.49% (Dice) and 64.60% (Jaccard), and I-KCEM achieved 78.55% (Dice) and 61.37% (Jaccard). Evaluation using 3D-ROC analysis also indicated that the DNN was the best detector based on metrics like target detection rate and overall effectiveness. The DNN model further exhibited the capability to identify tumor heterogeneity, differentiating high- and low-cellularity regions. Quantitative parameters, including apparent diffusion coefficient ($ADC$), pure diffusion coefficient ($D$), pseudo-diffusion coefficient ($D*$), and perfusion fraction ($PF$), were calculated and analyzed, providing insights into the diffusion characteristics of different breast tissues. Analysis of signal intensity decay curves generated from these parameters further illustrated distinct diffusion patterns and confirmed that high cellularity tumor regions showed greater water molecule confinement compared to low cellularity regions. Conclusions: This study highlights the potential of combining IVIM-DWI, hyperspectral imaging techniques, and deep learning as a robust, safe, and effective non-invasive diagnostic tool for breast cancer, offering a valuable alternative to contrast-enhanced methods by providing detailed information about tissue microstructure and heterogeneity without the need for contrast agents.