As the most frequently occurring cancer worldwide, breast cancer (BC) is the leading cause of cancer-related death in women. The overexpression of HER2 (human epidermal growth factor receptor 2) is found in about 15% of BC patients, and it is often associated with a poor prognosis due to the effect on cell proliferation, migration, invasion, and survival. As a result of the heterogeneity of BC, molecular imaging with HER2 probes can non-invasively, in real time, and quantitatively reflect the expression status of HER2 in tumors. This will provide a new approach for patients to choose treatment options and monitor treatment response. Furthermore, radionuclide molecular imaging has the potential of repetitive measurements, and it can help solve the problem of heterogeneous expression and conversion of HER2 status during disease progression or treatment. Different imaging probes of targeting proteins, such as monoclonal antibodies, antibody fragments, nanobodies, and affibodies, are currently in preclinical and clinical development. Moreover, in recent years, HER2-specific peptides have been widely developed for molecular imaging techniques for HER2-positive cancers. This article summarized different types of molecular probes targeting HER2 used in current clinical applications and the developmental trend of some HER2-specific peptides.

Background and purpose: This study sought to develop an advanced composite model to enhance the prognostic accuracy for cervical cancer patients undergoing concurrent chemoradiotherapy (CCRT). The model integrated imaging features from [¹⁸F]FDG PET/CT scans with inflammatory markers using a novel unsupervised two-way clustering approach.Methods: In this retrospective study, 154 patients diagnosed with primary cervical cancer and treated with CCRT were evaluated using [¹⁸F]FDG PET/CT scans. A total of 1,702 radiomic features were extracted from the imaging data. These features underwent rigorous selection based on reproducibility and non-redundancy. The unsupervised two-way clustering method was then employed to simultaneously stratify patients and reduce the dimensionality of features, resulting in the generation of meta-features that were subsequently used to predict overall survival.Results: Kaplan-Meier survival analysis demonstrated that the two-way clustering method successfully stratified patients into distinct risk groups with significant survival differences (P<0.001), outperforming traditional K-means clustering. Predictive models constructed using meta-features derived from two-way clustering showed superior performance compared to those using principal component analysis (PCA), particularly when more than four features were included. The highest C-index values for the COX, COX_Lasso, and RSF models were observed with nine meta-features, yielding results of 0.691 ± 0.026, 0.634 ± 0.018, and 0.684 ± 0.020, respectively. In contrast, models based solely on clinical variables exhibited lower predictive performance, with C-index values of 0.645 ± 0.041, 0.567 ± 0.016, and 0.561 ± 0.033. The combination of clinical data, inflammatory markers, and radiomic features achieved the highest predictive accuracy, with a mean AUC of 0.88 ± 0.07.Conclusion: Integrating radiomic data with inflammatory markers using unsupervised two-way clustering offered a robust approach for predicting survival outcomes in cervical cancer patients. This methodology presented a promising avenue for personalized patient management, potentially leading to more informed treatment decisions and improved outcomes.