A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model.

OBJECTIVE: To identify CT features and establish a nomogram, compared with a machine learning-based model for distinguishing gastrointestinal heterotopic pancreas (HP) from gastrointestinal stromal tumor (GIST). MATERIALS AND METHODS: This retrospective study included 148 patients with pathologically confirmed HP (n = 48) and GIST (n = 100) in the stomach or small intestine that were less than 3 cm in size. Clinical information and CT characteristics were collected. A nomogram on account of lasso regression and multivariate logistic regression, and a RandomForest (RF) model based on significant variables in univariate analyses were established. Receiver operating characteristic (ROC) curve, mean area under the curve (AUC), calibration curve and decision curve analysis (DCA) were carried out to evaluate and compare the diagnostic ability of models. RESULTS: The nomogram identified five CT features as independent predictors of HP diagnosis: age, location, LD/SD ratio, duct-like structure, and HU lesion/pancreas A. Five features were included in RF model and ranked according to their relevance to the differential diagnosis: LD/SD ratio, HU lesion/pancreas A, location, peritumoral hypodensity line and age. The nomogram and RF model yielded AUC of 0.951 (95% CI: 0.842-0.993) and 0.894 (95% CI: 0.766-0.966), respectively. The DeLong test found no statistically significant difference in diagnostic performance (p > 0.05), but DCA revealed that the nomogram surpassed the RF model in clinical usefulness. CONCLUSION: Two diagnostic prediction models based on a nomogram as well as RF method were reliable and easy-to-use for distinguishing between HP and GIST, which might also assist treatment planning.

Duodenal neuroendocrine neoplasms on enhanced CT: establishing a diagnostic model with duodenal gastrointestinal stromal tumors in the non-ampullary area and analyzing the value of predicting prognosis.

OBJECTIVE: To identify CT features and establish a diagnostic model for distinguishing non-ampullary duodenal neuroendocrine neoplasms (dNENs) from non-ampullary duodenal gastrointestinal stromal tumors (dGISTs) and to analyze overall survival outcomes of all dNENs patients. MATERIALS AND METHODS: This retrospective study included 98 patients with pathologically confirmed dNENs (n = 44) and dGISTs (n = 54). Clinical data and CT characteristics were collected. Univariate analyses and binary logistic regression analyses were performed to identify independent factors and establish a diagnostic model between non-ampullary dNENs (n = 22) and dGISTs (n = 54). The ROC curve was created to determine diagnostic ability. Cox proportional hazards models were created and Kaplan-Meier survival analyses were performed for survival analysis of dNENs (n = 44). RESULTS: Three CT features were identified as independent predictors of non-ampullary dNENs, including intraluminal growth pattern (OR 0.450; 95% CI 0.206-0.983), absence of intratumoral vessels (OR 0.207; 95% CI 0.053-0.807) and unenhanced lesion > 40.76 HU (OR 5.720; 95% CI 1.575-20.774). The AUC was 0.866 (95% CI 0.765-0.968), with a sensitivity of 90.91% (95% CI 70.8-98.9%), specificity of 77.78% (95% CI 64.4-88.0%), and total accuracy rate of 81.58%. Lymph node metastases (HR: 21.60), obstructive biliary and/or pancreatic duct dilation (HR: 5.82) and portal lesion enhancement ≤ 99.79 HU (HR: 3.02) were independent prognostic factors related to poor outcomes. CONCLUSION: We established a diagnostic model to differentiate non-ampullary dNENs from dGISTs. Besides, we found that imaging features on enhanced CT can predict OS of patients with dNENs.

Extracellular matrix modulating enzyme functionalized biomimetic Au nanoplatform-mediated enhanced tumor penetration and synergistic antitumor therapy for pancreatic cancer.

BACKGROUND: Excessive extracellular matrix (ECM) deposition in pancreatic ductal adenocarcinoma (PDAC) severely limits therapeutic drug penetration into tumors and is associated with poor prognosis. Collagen is the most abundant matrix protein in the tumor ECM, which is the main obstacle that severely hinders the diffusion of chemotherapeutic drugs or nanomedicines. METHODS: We designed a collagenase-functionalized biomimetic drug-loaded Au nanoplatform that combined ECM degradation, active targeting, immune evasion, near-infrared (NIR) light-triggered drug release, and synergistic antitumor therapy and diagnosis into one nanoplatform. PDAC tumor cell membranes were extracted and coated onto doxorubicin (Dox)-loaded Au nanocages, and then collagenase was added to functionalize the cell membrane through lipid insertion. We evaluated the physicochemical properties, in vitro and in vivo targeting, penetration and therapeutic efficacy of the nanoplatform. RESULTS: Upon intravenous injection, this nanoplatform efficiently targeted the tumor through the homologous targeting properties of the coated cell membrane. During penetration into the tumor tissue, the dense ECM in the PDAC tissues was gradually degraded by collagenase, leading to a looser ECM structure and deep penetration within the tumor parenchyma. Under NIR irradiation, both photothermal and photodynamic effects were produced and the encapsulated chemotherapeutic drugs were released effectively, exerting a strong synergistic antitumor effect. Moreover, this nanoplatform has X-ray attenuation properties that could serve to guide and monitor treatment by CT imaging. CONCLUSION: This work presented a unique and facile yet effective strategy to modulate ECM components in PDAC, enhance tumor penetration and tumor-killing effects and provide therapeutic guidance and monitoring.

Ultra-thin layered double hydroxide-mediated photothermal therapy combine with asynchronous blockade of PD-L1 and NR2F6 inhibit hepatocellular carcinoma.

BACKGROUND: The efficacy of immune checkpoint blockade (ICB), in the treatment of hepatocellular carcinoma (HCC), is limited due to low levels of tumor-infiltrating T lymphocytes and deficient checkpoint blockade in this immunologically "cool" tumor. Thus, combination approaches are needed to increase the response rates of ICB and induce synergistic antitumor immunity. METHODS: Herein, we designed a pH-sensitive multifunctional nanoplatform based on layered double hydroxides (LDHs) loaded with siRNA to block the intracellular immune checkpoint NR2F6, together with the asynchronous blockade surface receptor PD-L1 to induce strong synergistic antitumor immunity. Moreover, photothermal therapy (PTT) generated by LDHs after laser irradiation modified an immunologically "cold" microenvironment to potentiate Nr2f6-siRNA and anti-PD-L1 immunotherapy. Flow cytometry was performed to assess the immune responses initiated by the multifunctional nanoplatform. RESULTS: Under the slightly acidic tumor extracellular environment, PEG detached and the re-exposed positively charged LDHs enhanced tumor accumulation and cell uptake. The accumulated siRNA suppressed the signal of dual protumor activity in both immune and H22 tumor cells by silencing the NR2F6 gene, which further reduced the tumor burden and enhanced systemic antitumor immunity. The responses include enhanced tumor infiltration by CD4+ helper T cells, CD8+ cytotoxic T cells, and mature dendritic cells; the significantly decreased level of immune suppressed regulator T cells. The therapeutic responses were also attributed to the production of IL-2, IFN-γ, and TNF-α. The prepared nanoparticles also exhibited potential magnetic resonance imaging (MRI) ability, which could serve to guide synergistic immunotherapy treatment. CONCLUSIONS: In summary, the three combinations of PTT, NR2F6 gene ablation and anti-PD-L1 can promote a synergistic immune response to inhibit the progression of primary HCC tumors and prevent metastasis. This study can be considered a proof-of-concept for the targeting of surface and intracellular immune checkpoints to supplement the existing HCC immunotherapy treatments.

Machine Learning-Based Radiological Features and Diagnostic Predictive Model of Xanthogranulomatous Cholecystitis.

Background: Xanthogranulomatous cholecystitis (XGC) is a rare benign chronic inflammatory disease of the gallbladder that is sometimes indistinguishable from gallbladder cancer (GBC), thereby affecting the decision of the choice of treatment. Thus, this study aimed to analyse the radiological characteristics of XGC and GBC to establish a diagnostic prediction model for differential diagnosis and clinical decision-making. Methods: We investigated radiological characteristics confirmed by the RandomForest and Logistic regression to establish computed tomography (CT), magnetic resonance imaging (MRI), CT/MRI models and diagnostic prediction model, and performed receiver operating characteristic curve (ROC) analysis to prove the effectiveness of the diagnostic prediction model. Results: Based on the optimal features confirmed by the RandomForest method, the mean area under the curve (AUC) of the ROC of the CT and MRI models was 0.817 (mean accuracy = 0.837) and 0.839 (mean accuracy = 0.842), respectively, whereas the CT/MRI model had a considerable predictive performance with the mean AUC of 0.897 (mean accuracy = 0.906). The diagnostic prediction model established for the convenience of clinical application was similar to the CT/MRI model with the mean AUC and accuracy of 0.888 and 0.898, respectively, indicating a preferable diagnostic efficiency in distinguishing XGC from GBC. Conclusions: The diagnostic prediction model showed good diagnostic accuracy for the preoperative discrimination of XGC and GBC, which might aid in clinical decision-making.

A radiologic diagnostic scoring model based on CT features for differentiating gastric schwannoma from gastric gastrointestinal stromal tumors.

We aimed to further explore the CT features of gastric schwannoma (GS), propose and validate a convenient diagnostic scoring system to distinguish GS from gastric gastrointestinal stromal tumors (GISTs) preoperatively. 170 patients with submucosal tumors pathologically confirmed (GS n=35; gastric GISTs n=135) from Hospital 1 were analyzed retrospectively as the training cohort, and 72 patients (GS=11; gastric GISTs=61) from Hospital 2 were enrolled as the validation cohort. We searched for significant CT imaging characteristics and constructed the scoring system via binary logistic regression and converted regression coefficients to weighted scores. The ROC curves, AUCs and calibration tests were carried out to evaluate the scoring models in both the training cohort and the validation cohort. For convenient assessment, the system was further divided into four score ranges and their diagnostic probability of GS was calculated respectively. Four CT imaging characteristics were ultimately enrolled in this scoring system, including transverse position (2 points), location (5 points), perilesional lymph nodes (6 points) and pattern of enhancement (2 points). The AUC of the scoring model in the training cohort were 0.873 (95% CI, 0.816-0.929) and the cutoff point was 6 points. In the validation cohort, the AUC was 0.898 (95% CI, 0.804-0.957) and the cutoff value was 5 points. Four score ranges were as follows: 0-3 points for very low probability of GS, 4-7 points for low probability; 8-9 points for middle probability; 10-15 points for very high probability. A convenient scoring model to preoperatively discriminate GS from gastric GISTs was finally proposed.

Diagnostic Value of CT- and MRI-Based Texture Analysis and Imaging Findings for Grading Cartilaginous Tumors in Long Bones.

OBJECTIVE: To confirm the diagnostic performance of computed tomography (CT)-based texture analysis (CTTA) and magnetic resonance imaging (MRI)-based texture analysis for grading cartilaginous tumors in long bones and to compare these findings to radiological features. MATERIALS AND METHODS: Twenty-nine patients with enchondromas, 20 with low-grade chondrosarcomas and 16 with high-grade chondrosarcomas were included retrospectively. Clinical and radiological information and 9 histogram features extracted from CT, T1WI, and T2WI were evaluated. Binary logistic regression analysis was performed to determine predictive factors for grading cartilaginous tumors and to establish diagnostic models. Another 26 patients were included to validate each model. Receiver operating characteristic (ROC) curves were generated, and accuracy rate, sensitivity, specificity and positive/negative predictive values (PPV/NPV) were calculated. RESULTS: On imaging, endosteal scalloping, cortical destruction and calcification shape were predictive for grading cartilaginous tumors. For texture analysis, variance, mean, perc.01%, perc.10%, perc.99% and kurtosis were extracted after multivariate analysis. To differentiate benign cartilaginous tumors from low-grade chondrosarcomas, the imaging features model reached the highest accuracy rate (83.7%) and AUC (0.841), with a sensitivity of 75% and specificity of 93.1%. The CTTA feature model best distinguished low-grade and high-grade chondrosarcomas, with accuracies of 71.9%, and 80% in the training and validation groups, respectively; T1-TA and T2-TA could not distinguish them well. We found that the imaging feature model best differentiated benign and malignant cartilaginous tumors, with an accuracy rate of 89.2%, followed by the T1-TA feature model (80.4%). CONCLUSIONS: The imaging feature model and CTTA- or MRI-based texture analysis have the potential to differentiate cartilaginous tumors in long bones by grade. MRI-based texture analysis failed to grade chondrosarcomas.

Self-amplification of oxidative stress with tumour microenvironment-activatable iron-doped nanoplatform for targeting hepatocellular carcinoma synergistic cascade therapy and diagnosis.

BACKGROUND: Hepatocellular carcinoma is insensitive to many chemotherapeutic agents. Ferroptosis is a form of programmed cell death with a Fenton reaction mechanism. It converts endogenous hydrogen peroxide into highly toxic hydroxyl radicals, which inhibit hepatocellular carcinoma progression. METHODS: The morphology, elemental composition, and tumour microenvironment responses of various organic/inorganic nanoplatforms were characterised by different analytical methods. Their in vivo and in vitro tumour-targeting efficacy and imaging capability were analysed by magnetic resonance imaging. Confocal microscopy, flow cytometry, and western blotting were used to investigate the therapeutic efficacy and mechanisms of complementary ferroptosis/apoptosis mediated by the nanoplatforms. RESULTS: The nanoplatform consisted of a silica shell doped with iron and disulphide bonds and an etched core loaded with doxorubicin that generates hydrogen peroxide in situ and enhances ferroptosis. It relied upon transferrin for targeted drug delivery and could be activated by the tumour microenvironment. Glutathione-responsive biodegradability could operate synergistically with the therapeutic interaction between doxorubicin and iron and induce tumour cell death through complementary ferroptosis and apoptosis. The nanoplatform also has a superparamagnetic framework that could serve to guide and monitor treatment under T2-weighted magnetic resonance imaging. CONCLUSION: This rationally designed nanoplatform is expected to integrate cancer diagnosis, treatment, and monitoring and provide a novel clinical antitumour therapeutic strategy.