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Background: In this study, we developed a nomogram predictive model based on clinical, CT, and MRI parameters to differentiate soft tissue rhabdomyosarcoma (RMS) from neuroblastoma (NB) in children preoperatively. Materials and methods: A total of 103 children with RMS (n=37) and NB (n=66) were enrolled in the study from December 2012 to July 2023. The clinical and imaging data (assessed by two experienced radiologists) were analyzed using univariate analysis, and significant factors were further analyzed by multivariable logistic regression using the forward LR method to develop the clinical model, radiological model, and integrated nomogram model, respectively. The diagnostic performances, goodness of fit, and clinical utility of the integrated nomogram model were assessed using the area under the curve (AUC) of the receiver operator characteristics curve (ROC) with a 95% confidence interval (95% CI), calibration curve, and decision curve analysis (DCA) curves, respectively. Diagnostic efficacy between the model and radiologists' interpretations was examined. Results: The median age at diagnosis in the RMS group was significantly older than the NB group (36.0 months vs. 14.5 months; P=0.003); the fever rates in RMS patients were significantly lower than in patients with NB (0.0% vs.16.7%; P=0.022), and the incidence of palpable mass was higher in patients with RMS compared with the NB patients (89.2% vs. 34.8%; P<0.001). Compare NB on image features: RMS occurred more frequently in the head and neck and displayed homogeneous density on non-enhanced CT than NB (48.6% vs. 9.1%; 35.3% vs. 13.8%, respectively; all P<0.05), and the occurrence of characteristics such as calcification, encasing vessels, and intraspinal tumor extension was significantly less frequent in RMS children compared to children with NB (18.9% vs. 84.8%; 13.5% vs. 34.8%; 2.7% vs. 50.0%, respectively; all P <0.05). Two, three, and four features were identified as independent parameters by multivariate logistic regression analysis to develop the clinical, radiological, and integrated nomogram models, respectively. The AUC value (0.962), calibration curve, and DCA showed that the integrated nomogram model may provide better diagnostic performance, good agreement, and greater clinical net benefits than the clinical model, radiological model, and radiologists' subjective diagnosis. Conclusion: The clinical and imaging features-based nomogram has potential for helping radiologists distinguish between pediatric soft tissue RMS and NB patients preoperatively, and reduce unnecessary interventions.
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Objective: To assess the computed tomography (CT) and magnetic resonance (MR) imaging characteristics of soft tissue rhabdoid tumors (RT) and compare them with those of rhabdomyosarcoma (RMS). Methods: We conducted a retrospective analysis of 49 pediatric patients from 2011 to 2022, comprising 16 patients with soft tissue RT and 33 patients with RMS who underwent CT or MRI scans. Key imaging features, as well as clinical and pathological data, were compared between the two groups. The multivariate logistic regression analysis was used to determine independent differential factors for distinguishing soft tissue RT from RMS, and the model was established. The final prediction model was visualized by nomograms and verified internally by using a bootstrapped resample 1,000 times. The diagnostic accuracy of the combined model was assessed in terms of discrimination, calibration, and clinical utility. Results: Age, sex, number of lesions, and primary locations were similar in both groups. The imaging characteristics, including margin, calcification, surrounding blood vessels, and rim enhancement, were associated with the two groups of soft tissue tumors, as determined by univariate analysis (all p < 0.05). On multivariate logistic regression analysis, the presence of unclear margin (p-value, adjusted odds ratio [95% confidence interval]: 0.03, 7.96 [1.23, 51.67]) and calcification (0.012, 30.37 [2.09, 440.70]) were independent differential factors for predicting soft tissue RT over RMS. The presence of rim enhancement (0.007, 0.05 [0.01, 0.43]) was an independent differential factor for predicting RMS over soft tissue RT. The comprehensive model established by logistic regression analysis showed an AUC of 0.872 with 81.8% specificity and 81.3% sensitivity. The decision curve analysis (DCA) curve displayed that the model achieved a better net clinical benefit. Conclusion: Our study revealed that the image features of calcification, indistinct margins, and a lack of rim enhancement on CT and MRI might be reliable to distinguish soft tissue RT from RMS.
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Macrophage cell membrane-camouflaged nanocarriers can effectively reduce immune cell clearance and actively target tumors. In this study, a macrophage cell membrane-camouflaged mesoporous silica nanorod (MSNR)-based antitumor drug carrier equipped with a cationic polymer layer was developed. As drug carriers, these MSNRs were loaded with the thermosensitive phase change material L-menthol (LM), the chemotherapy drug doxorubicin (DOX) and the fluorescent molecule indocyanine green (ICG). The rod-like shape of the MSNRs was shown to enhance the penetration of the drug carriers to tumors. In the weakly acidic tumor microenvironment, the cationic polymer exhibited a proton sponge effect to trigger macrophage cell membrane coating detachment, promoting tumor cell uptake. Following nanocarrier uptake, ICG is heated by near-infrared (NIR) irradiation to make LM undergo a phase transition to release DOX and generate a synergistic effect of thermochemotherapy which kills tumor cells and inhibits tumor growth together with reactive oxygen species (ROS) produced by ICG. Overall, this nanohybrid drug delivery system demonstrates an intelligent cascade response, leads to tissue-cell specific targeting and improves drug release accuracy, thus proving to be an effective cancer therapy.