Radiomics based on 18F-FDG PET/CT for prediction of pathological complete response to neoadjuvant therapy in non-small cell lung cancer.

Purpose: This study aimed to establish and evaluate the value of integrated models involving 18F-FDG PET/CT-based radiomics and clinicopathological information in the prediction of pathological complete response (pCR) to neoadjuvant therapy (NAT) for non-small cell lung cancer (NSCLC). Methods: A total of 106 eligible NSCLC patients were included in the study. After volume of interest (VOI) segmentation, 2,016 PET-based and 2,016 CT-based radiomic features were extracted. To select an optimal machine learning model, a total of 25 models were constructed based on five sets of machine learning classifiers combined with five sets of predictive feature resources, including PET-based alone radiomics, CT-based alone radiomics, PET/CT-based radiomics, clinicopathological features, and PET/CT-based radiomics integrated with clinicopathological features. Area under the curves (AUCs) of receiver operator characteristic (ROC) curves were used as the main outcome to assess the model performance. Results: The hybrid PET/CT-derived radiomic model outperformed PET-alone and CT-alone radiomic models in the prediction of pCR to NAT. Moreover, addition of clinicopathological information further enhanced the predictive performance of PET/CT-derived radiomic model. Ultimately, the support vector machine (SVM)-based PET/CT radiomics combined clinicopathological information presented an optimal predictive efficacy with an AUC of 0.925 (95% CI 0.869-0.981) in the training cohort and an AUC of 0.863 (95% CI 0.740-0.985) in the test cohort. The developed nomogram involving radiomics and pathological type was suggested as a convenient tool to enable clinical application. Conclusions: The 18F-FDG PET/CT-based SVM radiomics integrated with clinicopathological information was an optimal model to non-invasively predict pCR to NAC for NSCLC.

A predictive radiotranscriptomics model based on DCE-MRI for tumor immune landscape and immunotherapy in cholangiocarcinoma.

This study aims to determine the predictive role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived radiomic model in tumor immune profiling and immunotherapy for cholangiocarcinoma. To perform radiomic analysis, immune related subgroup clustering was first performed by single sample gene set enrichment analysis (ssGSEA). Second, a total of 806 radiomic features for each phase of DCE-MRI were extracted by utilizing the Python package Pyradiomics. Then, a predictive radiomic signature model was constructed after a three-step features reduction and selection, and receiver operating characteristic (ROC) curve was employed to evaluate the performance of this model. In the end, an independent testing cohort involving cholangiocarcinoma patients with anti-PD-1 Sintilimab treatment after surgery was used to verify the potential application of the established radiomic model in immunotherapy for cholangiocarcinoma. Two distinct immune related subgroups were classified using ssGSEA based on transcriptome sequencing. For radiomic analysis, a total of 10 predictive radiomic features were finally identified to establish a radiomic signature model for immune landscape classification. Regarding to the predictive performance, the mean AUC of ROC curves was 0.80 in the training/validation cohort. For the independent testing cohort, the individual predictive probability by radiomic model and the corresponding immune score derived from ssGSEA was significantly correlated. In conclusion, radiomic signature model based on DCE-MRI was capable of predicting the immune landscape of chalangiocarcinoma. Consequently, a potentially clinical application of this developed radiomic model to guide immunotherapy for cholangiocarcinoma was suggested.

MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study.

Background: Accurate preoperative prediction of glioma is crucial for developing individualized treatment decisions and assessing prognosis. In this study, we aimed to establish and evaluate the value of integrated models by incorporating the intratumoral and peritumoral features from conventional MRI and clinical characteristics in the prediction of glioma grade. Methods: A total of 213 glioma patients from two centers were included in the retrospective analysis, among which, 132 patients were classified as the training cohort and internal validation set, and the remaining 81 patients were zoned as the independent external testing cohort. A total of 7728 features were extracted from MRI sequences and various volumes of interest (VOIs). After feature selection, 30 radiomic models depended on five sets of machine learning classifiers, different MRI sequences, and four different combinations of predictive feature sources, including features from the intratumoral region only, features from the peritumoral edema region only, features from the fusion area including intratumoral and peritumoral edema region (VOI-fusion), and features from the intratumoral region with the addition of features from peritumoral edema region (feature-fusion), were established to select the optimal model. A nomogram based on the clinical parameter and optimal radiomic model was constructed for predicting glioma grade in clinical practice. Results: The intratumoral radiomic models based on contrast-enhanced T1-weighted and T2-flair sequences outperformed those based on a single MRI sequence. Moreover, the internal validation and independent external test underscored that the XGBoost machine learning classifier, incorporating features extracted from VOI-fusion, showed superior predictive efficiency in differentiating between low-grade gliomas (LGG) and high-grade gliomas (HGG), with an AUC of 0.805 in the external test. The radiomic models of VOI-fusion yielded higher prediction efficiency than those of feature-fusion. Additionally, the developed nomogram presented an optimal predictive efficacy with an AUC of 0.825 in the testing cohort. Conclusion: This study systematically investigated the effect of intratumoral and peritumoral radiomics to predict glioma grading with conventional MRI. The optimal model was the XGBoost classifier coupled radiomic model based on VOI-fusion. The radiomic models that depended on VOI-fusion outperformed those that depended on feature-fusion, suggesting that peritumoral features should be rationally utilized in radiomic studies.

GPR81-mediated reprogramming of glucose metabolism contributes to the immune landscape in breast cancer.

BACKGROUND: Local tumor microenvironment (TME) plays a crucial role in immunotherapy for breast cancer (BC). Whereas, the molecular mechanism responsible for the crosstalk between BC cells and surrounding immune cells remains unclear. The present study aimed to determine the interplay between GPR81-mediated glucometabolic reprogramming of BC and the immune landscape in TME. MATERIALS AND METHODS: Immunohistochemistry (IHC) assay was first performed to evaluate the association between GPR81 and the immune landscape. Then, several stable BC cell lines with down-regulated GPR81 expression were established to directly identify the role of GPR81 in glucometabolic reprogramming, and western blotting assay was used to detect the underlying molecular mechanism. Finally, a transwell co-culture system confirmed the crosstalk between glucometabolic regulation mediated by GPR81 in BC and induced immune attenuation. RESULTS: IHC analysis demonstrated that the representation of infiltrating CD8+ T cells and FOXP3+ T cells were dramatically higher in BC with a triple negative (TN) subtype in comparison with that with a non-TN subtype (P < 0.001). Additionally, the ratio of infiltrating CD8+ to FOXP3+ T cells was significantly negatively associated with GPR81 expression in BC with a TN subtype (P < 0.001). Furthermore, GPR81 was found to be substantially correlated with the glycolytic capability (P < 0.001) of BC cells depending on a Hippo-YAP signaling pathway (P < 0.001). In the transwell co-culture system, GPR81-mediated reprogramming of glucose metabolism in BC significantly contributed to a decreased proportion of CD8+ T (P < 0.001) and an increased percentage of FOXP3+ T (P < 0.001) in the co-cultured lymphocytes. CONCLUSION: Glucometabolic reprogramming through a GPR81-mediated Hippo-YAP signaling pathway was responsible for the distinct immune landscape in BC. GPR81 was a potential biomarker to stratify patients before immunotherapy to improve BC's clinical prospect.

Smart Chemical Oxidative Polymerization Strategy To Construct Au@PPy Core-Shell Nanoparticles for Cancer Diagnosis and Imaging-Guided Photothermal Therapy.

Imaging-guided photothermal therapy (PTT) in a single nanoscale platform has aroused extensive research interest in precision medicine, yet only a few methods have gained wide acceptance. Thus, it remained an urgent need to facilely develop biocompatible and green probes with excellent theranostic capacity for superior biomedical applications. In this study, a smart chemical oxidative polymerization strategy was successfully developed for the synthesis of Au@PPy core-shell nanoparticles with polyvinyl alcohol (PVA) as the hydrophile. In the reaction, the reactant tetrachloroauric acid (HAuCl4) was reduced by pyrrole to fabricate a gold (Au) core, and pyrrole was oxidized to deposit around the Au core to form a polypyrrole (PPy) shell. The as-synthesized Au@PPy nanoparticles showed a regular core-shell morphology and good colloidal stability. Relying on the high X-ray attenuation of Au and strong near-infrared (NIR) absorbance of PPy and Au, Au@PPy nanoparticles exhibited excellent performance in blood pool/tumor imaging and PTT treatment by a series of in vivo experiments, in which tumor could be precisely positioned and thoroughly eradicated. Hence, the facile chemical oxidative polymerization strategy for constructing monodisperse Au@PPy core-shell nanoparticles with potential for cancer diagnosis and imaging-guided photothermal therapy shed light on an innovative design concept for the facile fabrication of biomedical materials.

Prediction of HER2 expression in breast cancer by combining PET/CT radiomic analysis and machine learning.

BACKGROUND: Human epidermal growth factor receptor 2 (HER2) expression status determination significantly contributes to HER2-targeted therapy in breast cancer (BC). The purpose of this study was to evaluate the role of radiomics and machine learning based on PET/CT images in HER2 status prediction, and to identify the most effective combination of machine learning model and radiomic features. METHODS: A total of 217 BC patients who underwent PET/CT examination were involved in the study and randomly divided into a training set (n = 151) and a testing set (n = 66). For all four models, the model parameters were determined using a threefold cross-validation in the training set. Each model's performance was evaluated on the independent testing set using the receiver operating characteristic (ROC) curve, and AUC was calculated to get a quantified performance measurement of each model. RESULTS: Among the four developed machine learning models, the XGBoost model outperformed other machine learning models in HER2 status prediction. Furthermore, compared to the XGBoost model based on PET alone or CT alone radiomic features, the predictive power for HER2 status by using XGBoost model based on PET/CTmean or PET/CTconcat radiomic fusion features was dramatically improved with an AUC of 0.76 (95% confidence interval [CI] 0.69-0.83) and 0.72 (0.65-0.80), respectively. CONCLUSIONS: The established machine learning classifier based on PET/CT radiomic features is potentially predictive of HER2 status in BC.

MOFs-Derived Fe-N Codoped Carbon Nanoparticles as O2-Evolving Reactor and ROS Generator for CDT/PDT/PTT Synergistic Treatment of Tumors.

Metal-organic frameworks (MOFs) derivatives had been widely explored in electronic and environmental fields, but rarely evaluated in the biomedical applications. Herein, Fe-N codoped carbon (FeNC) nanoparticles were synthesized and characterized via facile pyrolysis of precursor ZIF-8 (Fe/Zn) nanoparticles, and their potential applications in tumor therapy were assessed in this investigation both in vitro and in vivo. After PAA (sodium polyacrylate) modification, the FeNC@PAA nanoparticles were able to initiate a Fe-based Fenton-like reaction to generate ·OH and O2 for chemodynamic therapy (CDT) and O2 evolution. Meanwhile, the porphyrin-like metal center in the FeNC@PAA nanoparticles could be used as a photosensitizer for photodynamic therapy (PDT) of tumors, which could be enhanced by O2 generated in CDT. Furthermore, the FeNC@PAA nanoparticles were also found to be effective in photothermal therapy (PTT) with a photothermal conversion efficiency of 29.15%, owing to a high absorbance in the near-infrared region (NIR). In conclusion, the synthesized FeNC@PAA nanoparticles exhibited promising applications in O2 evolution and CDT/PDT/PTT synergistic treatment of tumors.