With the continuous advancements in tumor immunotherapy, researchers are actively exploring new treatment methods. Peptide therapeutic cancer vaccines have garnered significant attention for their potential in improving patient outcomes. Despite its potential, only a single peptide-based cancer vaccine has been approved by the U.S. Food and Drug Administration (FDA). A comprehensive understanding of the underlying mechanisms and current development status is crucial for advancing these vaccines. This review provides an in-depth analysis of the production principles and therapeutic mechanisms of peptide-based cancer vaccines, highlights the commonly used peptide-based cancer vaccines, and examines the synergistic effects of combining these vaccines with immunotherapy, targeted therapy, radiotherapy, and chemotherapy. While some studies have yielded suboptimal results, the potential of combination therapies remains substantial. Additionally, we addressed the management and adverse events associated with peptide-based cancer vaccines, noting their relatively higher safety profile compared to traditional radiotherapy and chemotherapy. Lastly, we also discussed the roles of adjuvants and targeted delivery systems in enhancing vaccine efficacy. In conclusion, this review comprehensively outlines the current landscape of peptide-based cancer vaccination and underscores its potential as a pivotal immunotherapy approach.

Purpose: The purpose of this study was to evaluate the diagnostic accuracy when using various base material pairs (BMPs) in dual-energy computed tomography (DECT), and to establish corresponding diagnostic standards for assessing bone status through comparison with quantitative computed tomography (QCT). Methods: This prospective study enrolled a total of 469 patients who underwent both non-enhanced chest CT scans under conventional kVp and abdominal DECT. The bone densities of hydroxyapatite (water), hydroxyapatite (fat), hydroxyapatite (blood), calcium (water), and calcium (fat) (D_{HAP (water)}, D_{HAP (fat)}, D_{HAP (blood)}, D_{Ca (water)}, and D_{Ca (fat)}) in the trabecular bone of vertebral bodies (T11–L1) were measured, along with bone mineral density (BMD) via QCT. Intraclass correlation coefficient (ICC) analysis was used to assess the agreement of the measurements. Spearman’s correlation test was performed to analyze the relationship between the DECT- and QCT-derived BMD. Receiver operator characteristic (ROC) curves were generated to determine the optimal diagnostic thresholds of various BMPs for diagnosing osteopenia and osteoporosis. Results: A total of 1371 vertebral bodies were measured, and QCT identified 393 with osteoporosis and 442 with osteopenia. Strong correlations were observed between D_{HAP (water)}, D_{HAP (fat)}, D_{HAP (blood)}, D_{Ca (water)}, and D_{Ca (fat)} and the QCT-derived BMD. D_{HAP (water)} showed the best predictive capability for osteopenia and osteoporosis. The area under the ROC curve, sensitivity, and specificity for identifying osteopenia were 0.956, 86.88%, and 88.91% with D_{HAP (water)} ≤ 107.4 mg/cm³, respectively. The corresponding values for identifying osteoporosis were 0.999, 99.24%, and 99.53% with D_{HAP (water)} ≤ 89.62 mg/cm³, respectively. Conclusions: Bone density measurement using various BMPs in DECT enables the quantification of vertebral BMD and the diagnosis of osteoporosis, with D_{HAP (water)} having the highest diagnostic accuracy.

To assess the predictive value of intratumoral and peritumoral radiomics based on Dual-energy CT urography (DECTU) multi-images for preoperatively predicting the muscle invasion status of bladder cancer (BCa). This retrospective analysis involved 202 BCa patients who underwent DECTU. DECTU-derived quantitative parameters were identified as risk factors through stepwise regression analysis to construct a DECT model. The radiomic features from the intratumoral and 3 mm outward peritumoral regions were extracted from the 120 kVp-like, 40 keV, 100 keV, and iodine-based material-decomposition (IMD) images in the venous-phase and were screened using Mann-Whitney U test, Spearman correlation analysis, and LASSO. Radiomics models were developed using the Multilayer Perceptron for the intratumoral, peritumoral and intra- and peritumoral (IntraPeri) regions. Subsequently, a nomogram was created by integrating the multi-image IntraPeri radiomics and DECT model. Model performance was evaluated using area-under-the-curve (AUC), accuracy, sensitivity, and specificity. Normalized iodine concentration (NIC) was identified as an independent predictor for the DECT model. The IntraPeri model demonstrated superior performance compared to the intratumoral and peritumoral models both in 40 keV (0.830 vs. 0.766 vs. 0.763) and IMD images (0.881 vs. 0.840 vs. 0.821) in the test cohort. In the test cohort, the nomogram exhibited the best predictability (AUC=0.886, accuracy=0.836, sensitivity=0.737, and specificity=0.881), outperformed the DECT model (AUC=0.763, accuracy=0.754, sensitivity=0.632, and specificity=0.810) in predicting muscle invasion status of BCa with a statistically significant difference (p < 0.05). The nomogram, incorporating IntraPeri radiomics and NIC, serves as a valuable and non-invasive tool for preoperatively assessing the muscle invasion status of BCa.

Background: Many patients with malignant tumors require chemotherapy and radiation therapy, which can result in a decline in physical function and potentially influence bone mineral density (BMD). Furthermore, these treatments necessitate enhanced computed tomography (CT) scans for determining disease staging or treatment outcomes, and opportunistic screening with available imaging data is beneficial for patients at high risk for osteoporosis if existing imaging data can be used. The study aimed to investigate the feasibility of opportunistic screening for osteoporosis using enhanced CT based on a dual-energy CT (DECT) material decomposition technique. Methods: We prospectively enrolled 346 consecutive patients who underwent abdominal unenhanced and triphasic contrast-enhanced CT (arterial, portal venous, and delayed phases) between June 2021 and June 2022. The BMD, and the density of hydroxyapatite (HAP) on HAP-iodine images and calcium (Ca) on Ca-iodine images were measured on the L1–L3 vertebral bodies. The iodine intake was recorded. Pearson analysis was conducted to assess the correlation between iodine intake and the density values in three phases and the correlation between BMD and the densities of HAP and Ca. Furthermore, linear regression was employed for quantitative evaluation. Bland-Altman analysis was used to evaluate the agreement between calculated BMD derived from DECT (BMD-DECT) and reference BMD derived from quantitative CT (BMD-QCT). Receiver operating characteristic (ROC) analysis was applied to assess the diagnostic efficacy. Results: The HAP and Ca density of the L1–L3 vertebral bodies did not differ significantly among the three phases of contrast-enhanced CT (F=0.001–0.049; P>0.05). Significant positive correlations were found between HAP, Ca densities, and BMD (HAP-BMD: r=0.9472, R²=0.8973; Ca-BMD: r=0.9470, R²=0.8968; all P<0.001). Bland-Altman plots showed high agreement between BMD-DECT and BMD-QCT. The area under the curve (AUC) using HAP and Ca measurements was 0.963 [95% confidence interval (CI): 0.937–0.980] and 0.964 (95% CI: 0.939–0.981), respectively, for diagnosing osteoporosis and was 0.951 (95% CI: 0.917–0.973) and 0.950 (95% CI: 0.916–0.973), respectively, for diagnosing osteopenia. Conclusions: The HAP and Ca density measurements generated through the material decomposition technique in DECT have good diagnostic performances in assessing BMD, which offers a new perspective for opportunistic screening of osteoporosis on contrast-enhanced CT.