Predicting Microvascular Invasion in Hepatocellular Carcinoma Using CT-based Radiomics Model.

Background Prediction of microvascular invasion (MVI) may help determine treatment strategies for hepatocellular carcinoma (HCC). Purpose To develop a radiomics approach for predicting MVI status based on preoperative multiphase CT images and to identify MVI-associated differentially expressed genes. Materials and Methods Patients with pathologically proven HCC from May 2012 to September 2020 were retrospectively included from four medical centers. Radiomics features were extracted from tumors and peritumor regions on preoperative registration or subtraction CT images. In the training set, these features were used to build five radiomics models via logistic regression after feature reduction. The models were tested using internal and external test sets against a pathologic reference standard to calculate area under the receiver operating characteristic curve (AUC). The optimal AUC radiomics model and clinical-radiologic characteristics were combined to build the hybrid model. The log-rank test was used in the outcome cohort (Kunming center) to analyze early recurrence-free survival and overall survival based on high versus low model-derived score. RNA sequencing data from The Cancer Image Archive were used for gene expression analysis. Results A total of 773 patients (median age, 59 years; IQR, 49-64 years; 633 men) were divided into the training set (n = 334), internal test set (n = 142), external test set (n = 141), outcome cohort (n = 121), and RNA sequencing analysis set (n = 35). The AUCs from the radiomics and hybrid models, respectively, were 0.76 and 0.86 for the internal test set and 0.72 and 0.84 for the external test set. Early recurrence-free survival (P < .01) and overall survival (P < .007) can be categorized using the hybrid model. Differentially expressed genes in patients with findings positive for MVI were involved in glucose metabolism. Conclusion The hybrid model showed the best performance in prediction of MVI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Summers in this issue.

Sequential drug delivery by injectable macroporous hydrogels for combined photodynamic-chemotherapy.

With hollow mesoporous silica (hMSN) and injectable macroporous hydrogel (Gel) used as the internal and external drug-loading material respectively, a sequential drug delivery system DOX-CA4P@Gel was constructed, in which combretastatin A4 phosphate (CA4P) and doxorubicin (DOX) were both loaded. The anti-angiogenic drug, CA4P was initially released due to the degradation of Gel, followed by the anti-cell proliferative drug, DOX, released from hMSN in tumor microenvironment. Results showed that CA4P was mainly released at the early stage. At 48 h, CA4P release reached 71.08%, while DOX was only 24.39%. At 144 h, CA4P was 78.20%, while DOX release significantly increased to 61.60%, showing an obvious sequential release behavior. Photodynamic properties of porphyrin endow hydrogel (ϕΔ(Gel) = 0.91) with enhanced tumor therapy effect. In vitro and in vivo experiments showed that dual drugs treated groups have better tumor inhibition than solo drug under near infrared laser irradiation, indicating the effectivity of combined photodynamic-chemotherapy.

Prediction of preoperative microvascular invasion by dynamic radiomic analysis based on contrast-enhanced computed tomography.

PURPOSE: Microvascular invasion (MVI) is a common complication of hepatocellular carcinoma (HCC) surgery, which is an important predictor of reduced surgical prognosis. This study aimed to develop a fully automated diagnostic model to predict pre-surgical MVI based on four-phase dynamic CT images. METHODS: A total of 140 patients with HCC from two centers were retrospectively included (training set, n = 98; testing set, n = 42). All CT phases were aligned to the portal venous phase, and were then used to train a deep-learning model for liver tumor segmentation. Radiomics features were extracted from the tumor areas of original CT phases and pairwise subtraction images, as well as peritumoral features. Lastly, linear discriminant analysis (LDA) models were trained based on clinical features, radiomics features, and hybrid features, respectively. Models were evaluated by area under curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV). RESULTS: Overall, 86 and 54 patients with MVI- (age, 55.92 ± 9.62 years; 68 men) and MVI+ (age, 53.59 ± 11.47 years; 43 men) were included. Average dice coefficients of liver tumor segmentation were 0.89 and 0.82 in training and testing sets, respectively. The model based on radiomics (AUC = 0.865, 95% CI: 0.725-0.951) showed slightly better performance than that based on clinical features (AUC = 0.841, 95% CI: 0.696-0.936). The classification model based on hybrid features achieved better performance in both training (AUC = 0.955, 95% CI: 0.893-0.987) and testing sets (AUC = 0.913, 95% CI: 0.785-0.978), compared with models based on clinical and radiomics features (p-value < 0.05). Moreover, the hybrid model also provided the best accuracy (0.857), sensitivity (0.875), and NPV (0.917). CONCLUSION: The classification model based on multimodal intra- and peri-tumoral radiomics features can well predict HCC patients with MVI.

Tumor-Associated Macrophages Regulating a Polymer Nanoplatform for Synergistic Treatment of Breast Tumors.

Tumor-associated macrophages (TAMs) play a critical role in tumor progression and metastasis. Modulation of TAM polarization is one of the most effective strategies to change the immunosuppressive tumor microenvironment (TME). In this study, organic polymer nanoparticles (CPHT) were prepared using hyaluronic acid (HA)-conjugated disulfide-bonded polyethylene imide (PEIS) as a carrier through a self-assembly strategy. These nanoparticles were modified by transferrin (Tf) and loaded with chlorin e6 (Ce6). The results showed that CPHT had good dispersion with a particle size of about 30 nm. CPHT gradually disintegrated under the exposure with a high concentration of glutathione (GSH) in tumor cells, proving the possibility for the controlled release of Ce6 and photodynamic therapy. An in vitro test showed that the uptake of CPHT in tumor cells was mediated by both HA and Tf, indicating the active tumor-targeting capacity of CPHT. CPHT significantly downregulated the ratio of CD206/CD86 and triggered the upregulation of immune factors such as TNF-α and iNOS, suggesting the repolarization of TAMs. We also found that CPHT effectively induced ferroptosis in tumor cells through lipid peroxide accumulation, GSH depletion, and downregulation of lipid peroxidase (GPX4) expression. Animal experiments confirmed that CPHT not only effectively inhibited the growth of tumors in situ but also significantly decelerated the growth of the distal tumor. Elevated levels of CD86 and IFN-γ and decreased expression of CD206 were observed at the tumor sites post CPHT treatment. These results confirmed the value of CPHT as a multifunctional nanoplatform that can tune the TME and provide new hope for tumor treatment.

Fully automated magnetic resonance imaging-based radiomics analysis for differentiating pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma.

PURPOSE: To develop and validate an automated magnetic resonance imaging (MRI)-based model to preoperatively differentiate pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC). METHODS: This retrospective study included patients with surgically resected, histopathologically confirmed PASC or PDAC who underwent MRI between January 2011 and December 2020. According to time of treatment, they were divided into training and validation sets. Automated deep-learning-based artificial intelligence was used for pancreatic tumor segmentation. Linear discriminant analysis was performed with conventional MRI and radiomic features to develop clinical, radiomics, and mixed models in the training set. The models' performances were determined from their discrimination and clinical utility. Kaplan-Meier and log-rank tests were used for survival analysis. RESULTS: Overall, 389 and 123 patients with PDAC (age, 61.37 ± 9.47 years; 251 men) and PASC (age, 61.99 ± 9.82 years; 78 men) were included, respectively; they were split into the training (n = 358) and validation (n = 154) sets. The mixed model showed good performance in the training and validation sets (area under the curve: 0.94 and 0.96, respectively). The sensitivity, specificity, and accuracy were 76.74%, 93.38%, and 89.39% for the training set, respectively, and 67.57%, 97.44%, and 90.26% for the validation set, respectively. The mixed model outperformed the clinical (p = 0.001) and radiomics (p = 0.04) models in the validation set. Log-rank test revealed significantly longer survival in the predicted PDAC group than in the predicted PASC group (p = 0.003), according to the mixed model. CONCLUSION: Our mixed model, which combined MRI and radiomic features, can be used to differentiate PASC from PDAC.