Breast cancer (BC) is the most common malignant tumor in women worldwide, which seriously threatens women’s physical and mental health. In recent years, photodynamic therapy (PDT) has shown significant advantages in cancer treatment. PDT involves activating photosensitizers with appropriate wavelengths of light, producing transient levels of reactive oxygen species (ROS). Compared with free photosensitizers, the use of nanoparticles in PDT shows great advantages in terms of solubility, early degradation, and biodistribution, as well as more effective intercellular penetration and targeted cancer cell uptake. Under the current circumstances, researchers have made promising efforts to develop nanocarrier photosensitizers. Reasonably designed photosensitizer (PS) nanoparticles can be achieved through non-covalent (self-aggregation, interfacial deposition, interfacial polymerization or core-shell embedding and physical adsorption) or covalent (chemical immobilization or coupling) processes and accumulate in certain tumors through passive and/or active targeting. These PS loading methods provide chemical and physical stability to the PS payload. Among nanoparticles, metal nanoparticles have the advantages of high stability, adjustable size, optical properties, and easy surface functionalization, making them more biocompatible in biological applications. In this review, we summarize the current development and application status of photodynamic therapy for breast cancer, especially the latest developments in the application of metal nanocarriers in breast cancer PDT, and highlight some of the recent synergistic therapies, hopefully providing an accessible overview of the current knowledge that may act as a basis for new ideas or systematic evaluations of already promising results.

Purpose：Positron emission tomography (PET) with integrated computed tomography (PET/CT) is a whole-body imaging method providing information the entire body. When it was used in staging breast cancer patients, quite a few patients were found to have a second primary lung cancer(PLC), which was has few distinguishing features from breast cancer metastasis(MBC). Therefore, based on CT, LDCT and PET images, combined with pathological features, we established radiomics models to distinguish between MBC and PLC.Methods：We retrospectively collected CT, LDCT, and PET images, and pathology features of 100 breast cancer patients, including 60 metastases of breast cancer(MBC) and 40 primary lung cancers(PLC). The two radiologists manually drew a region of interest around the whole visible tumor in consensus. Python 3.8 and Pyradiomics toolkit are used to extract features from CT, LDCT, and PET. The linear discriminant analysis (LDA) classifier was used to build the radiomics model. The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to evaluate the classification performance.Results：Total 12, 13, and 9 features were selected from the CT, LDCT, and PET respectively. The model based on the LDCT and PET obtained the same highest AUC (0.9479). The combination with CT and pathology features showed a highest AUC of 0.9583 with a sensitivity of 1.000 and a specificity of 0.8333. Conclusion：Overall, the results are encouraging that radiomics models based on CT, LDCT and PET can differentiate between MBC and PLC pathological features could significantly improve the AUC and ACC of CT model.

Objectives: Platinum-based neoadjuvant therapy can improve pathological response in triple-negative breast cancer (TNBC), but severe hematotoxicity may compromise treatment delivery. This study developed and validated a multitask learning framework to jointly predict pathological complete response (pCR) and severe hematotoxicity and to support benefit–hematotoxicity stratification. Methods: This multicenter retrospective study included 2060 consecutive patients with TNBC receiving platinum-based neoadjuvant therapy at three institutions. Patients were assigned to a training cohort (n = 1406), an internal validation cohort (n = 351), or an external validation cohort (n = 303). A multitask TabNet (MT-TabNet) model was developed using pretreatment clinical, pathological, imaging, laboratory, electrocardiographic, and planned treatment exposure variables to jointly estimate pCR and severe hematotoxicity. Model performance was evaluated using discrimination, calibration, decision curve analysis, and interpretability analyses. The predicted probabilities were further integrated into a utility-based framework for benefit–hematotoxicity stratification. Results: Overall, 639 patients (31.0%) achieved pCR, and 651 (31.6%) developed severe hematotoxicity. MT-TabNet achieved AUCs of 0.874, 0.842, and 0.819 for pCR prediction and 0.859, 0.846, and 0.818 for severe hematotoxicity prediction in the training, internal validation, and external validation cohorts, respectively. The model showed generally acceptable calibration and clinical net benefit. pCR prediction was predominantly associated with tumor-related characteristics, whereas severe hematotoxicity prediction was more strongly associated with planned treatment exposure and host-related laboratory indicators. Utility-based stratification showed progressively higher pCR rates and lower severe hematotoxicity rates from the low- to high-benefit groups across all three cohorts. Survival differed significantly among benefit groups for both progression-free survival (PFS) and overall survival (OS) in the training and internal validation cohorts; in the external validation cohort, OS differed significantly, whereas PFS showed a similar but nonsignificant trend. Conclusions: MT-TabNet enabled joint estimation of pCR and severe hematotoxicity in patients with TNBC receiving platinum-based neoadjuvant therapy. The utility-based framework integrated efficacy and toxicity predictions into clinically interpretable benefit–hematotoxicity stratification and warrants further prospective evaluation for individualized risk assessment and toxicity monitoring.