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: Differential diagnosis of single-nodule pulmonary metastasis (SNPM) and second primary lung cancer (SPLC) in patients with colorectal cancer (CRC) prior to lung surgery is relatively complex. Radiomics is an emerging technique for image information analysis, while it has not yet been applied to construct a differential diagnostic model between SNPM and SPLC in patients with CRC. In the present study, we aimed to extract radiomics signatures from thin-section computed tomography (CT) images of the chest. These radiomics signatures were combined with clinical features to construct a composite differential diagnostic model. Method: A total of 91 patients with CRC, including 66 patients with SNPM and 25 patients with SPLC, were enrolled in this study. Patients were randomly assigned to the training cohort (n = 63) and validation cohort (n = 28) at a ratio of 7 to 3. Moreover, 107 radiomics features were extracted from the chest thin-section CT images. The least absolute shrinkage and selection operator (LASSO) regression was used to filter these features, and clinical features were screened by univariate analysis. The screened radiomics and clinical features were combined to construct a multifactorial logistic regression composite model. The receiver operating characteristic (ROC) curves were adopted to evaluate the models, and the corresponding nomograms were created. Results: A series of 6 radiomics characteristics was screened by LASSO. After univariate logistic regression analysis, the composite model finally included 4 radiomics features and 4 clinical features. In the training cohort, the area under the curve scores of ROC curves were 0.912 (95% confidence interval [CI]: 0.813-0.969), 0.884 (95% CI: 0.778-0.951), and 0.939 (95% CI: 0.848-0.984) for models derived from radiomics, clinical, and combined features, respectively. Similarly, these values were 0.756 (95% CI: 0.558-0.897), 0.888 (95% CI: 0.711-0.975), and 0.950 (95% CI: 0.795-0.997) in the validation cohort, respectively. Conclusions: We constructed a model for differential diagnosis of SNPM and SPLC in patients with CRC using radiomics and clinical features. Moreover, our findings provided a new assessment tool for patients with CRC in the future.

Objective. Solitary pulmonary lesions (SPNs) in patients with a history of colorectal cancer (CRC) may be attributed to metastatic lung tumors, primary lung cancer, or benign nodules. We aimed to analyze the imaging characteristics of SPNs in CRC patients to differentiate these pulmonary nodules and evaluate the prognostic value of isolated pulmonary metastasis from CRC using ¹⁸F‐FDG PET/CT. Methods. From January 2013 to January 2021, 62 CRC patients with SPNs demonstrated with ¹⁸F‐FDG PET/CT were retrospectively enrolled in the present study. We compared the radiological and clinical characteristics of these patients. In addition, survival time and prognostic factors were statistically analyzed using the Kaplan‐Meier method and multivariable Cox proportional hazards models. Results. There were 33 cases of isolated lung metastasis, 20 cases of second primary lung cancer (SPLC), and nine cases of benign nodules. The proportion of nodules with a maximal diameter greater than the median value was lower in the isolated lung metastasis group compared with the SPLC group (p < 0.05), showing polygonal shape, ill‐defined margin, pleural indentation, air bronchogram, speculation, and ground‐glass opacity. Patients with isolated lung metastasis had a significantly higher maximal diameter of lung lesion, SUV_max of lung lesion, and ¹⁸F‐FDG uptake compared with the benign nodule group (p < 0.05). Multivariate analysis revealed that the following two factors were significant independent predictors of PLC: air bronchogram (hazard ratio [HR] =22.327; 95% confidence interval [CI]: 1.910‐261.061; p = 0.013) and spiculation (HR =6.148; 95% CI 1.469‐25.725; p = 0.013). Initial TNM stage IV (HR =19.831, 95% CI 1.061‐370.782; p = 0.046) was extremely associated with a decreased lifespan of CRC patients with isolated lung metastasis. Conclusions. The result showed that CT features, including air bronchogram and spiculated margins, could be used to differentiate SPLC from single isolated lung metastasis in CRC patients. In patients with isolated lung metastasis, primary CRC TNM stage IV was associated with a poorer prognosis, and patients with such conditions might need more care.