Nrf2 expression, mitochondrial fission, and neuronal apoptosis in the prefrontal cortex of methamphetamine abusers and rats.

Methamphetamine (MA), a representative amphetamine-type stimulant, is one of the most abused drugs worldwide. Studies have shown that MA-induced neurotoxicity is strongly associated with oxidative stress and apoptosis. While nuclear factor E2-related factor 2 (Nrf2), an antioxidant transcription factor, is known to exert neuroprotective effects, its role in MA-induced dopaminergic neuronal apoptosis remains incompletely understood. In the present study, we explored the effects of MA on the expression levels of Nrf2, dynamin-related protein 1 (Drp1), mitofusin 1 (Mfn1), cytochrome c oxidase (Cyt-c), and cysteine aspartate-specific protease 3 (Caspase 3), as well as the correlations between Nrf2 and mitochondrial dynamics and apoptosis. Brain tissue from MA abusers was collected during autopsy procedures. An MA-dependent rat model was also established by intraperitoneal administration of MA (10 mg/kg daily) for 28 consecutive days, followed by conditioned place preference (CPP) testing. Based on immunohistochemical staining and western blot analysis, the protein expression levels of Nrf2 and Mfn1 showed a decreasing trend, while levels of Drp1, Cyt-c, and Caspase 3 showed an increasing trend in the cerebral prefrontal cortex of both MA abusers and MA-dependent rats. Notably, the expression of Nrf2 was positively associated with the expression of Mfn1, but negatively associated with the expression levels of Drp1, Cyt-c, and Caspase 3. These findings suggest that oxidative stress and mitochondrial fission contribute to neuronal apoptosis, with Nrf2 potentially playing a critical role in MA-induced neurotoxicity.

Classification and prediction of chemoradiotherapy response and survival from esophageal carcinoma histopathology images.

Whole slide imaging (WSI) of Hematoxylin and Eosin-stained biopsy specimens has been used to predict chemoradiotherapy (CRT) response and overall survival (OS) of esophageal squamous cell carcinoma (ESCC) patients. This retrospective study collected 279 specimens in 89 non-surgical ESCC patients through endoscopic biopsy between January 2010 and January 2019. These patients were divided into a CRT response group (CR + PR group) and a CRT non-response group (SD + PD group). The WSIs have segmented approximately 1,206,000 non-overlapping patches. Two experienced pathologists manually delineated the eight types of tissues on 32 WSIs, including esophagus tumor cell (TUM), cancer-associated stroma (CAS), normal epithelium layer (NEL), smooth muscle (MUS), lymphocytes (LYM), Red cells (RED), debris (DEB), uneven areas (UNE). The chemoradiotherapy response prediction models were built using maximum relevance-minimum redundancy (MRMR) feature selection and least absolute shrinkage and selection operator (LASSO) regression. However, pathological features with p < 0.1 were selected and integrated to be further screened using a LASSO Cox regression model to build a multivariate Cox proportional hazards model for predicting the OS. The testing accuracy of the tissue classification model was 91.3 %. The pathological model created using two CAS in-depth features and eight TUM in-depth features performed best for the prediction of treatment response and achieved an AUC of 0.744. For the prediction of OS, the testing AUC of this model at one year and three years were 0.675 and 0.870, respectively. The TUM model showed the highest AUC at one year (0.712). With its high accuracy rate, the deep learning model has the potential to transform from bench to bedside in clinical practice, improve patient's quality of life, and prolong the OS rate.

Radiotherapy in Preclinical Models of Brain Metastases: A Review and Recommendations for Future Studies.

Brain metastases (BMs) frequently occur in primary tumors such as lung cancer, breast cancer, and melanoma, and are associated with notably short natural survival. In addition to surgical interventions, chemotherapy, targeted therapy, and immunotherapy, radiotherapy (RT) is a crucial treatment for BM and encompasses whole-brain radiotherapy (WBRT) and stereotactic radiosurgery (SRS). Validating the efficacy and safety of treatment regimens through preclinical models is imperative for successful translation to clinical application. This not only advances fundamental research but also forms the theoretical foundation for clinical study. This review, grounded in animal models of brain metastases (AM-BM), explores the theoretical underpinnings and practical applications of radiotherapy in combination with chemotherapy, targeted therapy, immunotherapy, and emerging technologies such as nanomaterials and oxygen-containing microbubbles. Initially, we provided a concise overview of the establishment of AM-BMs. Subsequently, we summarize key RT parameters (RT mode, dose, fraction, dose rate) and their corresponding effects in AM-BMs. Finally, we present a comprehensive analysis of the current research status and future directions for combination therapy based on RT. In summary, there is presently no standardized regimen for AM-BM treatment involving RT. Further research is essential to deepen our understanding of the relationships between various parameters and their respective effects.

Radiomics and deep learning models to differentiate lung adenosquamous carcinoma: A multicenter trial.

Adenosquamous carcinoma (ASC) is frequently misdiagnosed or overlooked in clinical practice due to its dual histological components and potential transformation from either adenocarcinoma (ADC) or squamous cell carcinoma (SCC). Our study aimed to differentiate ASC from ADC and SCC by incorporating features of enhanced CTs and clinical characteristics to build radiomics and deep learning models. The classification models were trained in Xiangya Hospital and validated in two other independent hospitals. The areas under the receiver operating characteristic curves (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to estimate the performance. The optimal three-class classification model achieved a maximum AUC of 0.89 and accuracy of 0.81 in external validation sets, AUC of 0.99 and accuracy of 0.99 in the internal test set. These findings highlight the efficacy of our models in differentiating ASC, providing a non-invasive, timely, and accurate diagnostic approach before and during the treatment.

CT-based radiomics for predicting radio-chemotherapy response and overall survival in nonsurgical esophageal carcinoma.

Background: To predict treatment response and 2 years overall survival (OS) of radio-chemotherapy in patients with esophageal cancer (EC) by radiomics based on the computed tomography (CT) images. Methods: This study retrospectively collected 171 nonsurgical EC patients treated with radio-chemotherapy from Jan 2010 to Jan 2019. 80 patients were randomly divided into training (n=64) and validation (n=16) cohorts to predict the radiochemotherapy response. The models predicting treatment response were established by Lasso and logistic regression. A total of 156 patients were allocated into the training cohort (n=110), validation cohort (n=23) and test set (n=23) to predict 2-year OS. The Lasso Cox model and Cox proportional hazards model established the models predicting 2-year OS. Results: To predict the radiochemotherapy response, WFK as a radiomics feature, and clinical stages and clinical M stages (cM) as clinical features were selected to construct the clinical-radiomics model, achieving 0.78 and 0.75 AUC (area under the curve) in the training and validation sets, respectively. Furthermore, radiomics features called WFI and WGI combined with clinical features (smoking index, pathological types, cM) were the optimal predictors to predict 2-year OS. The AUC values of the clinical-radiomics model were 0.71 and 0.70 in the training set and validation set, respectively. Conclusions: This study demonstrated that planning CT-based radiomics showed the predictability of the radiochemotherapy response and 2-year OS in nonsurgical esophageal carcinoma. The predictive results prior to treatment have the potential to assist physicians in choosing the optimal therapeutic strategy to prolong overall survival.

Whole slide imaging-based deep learning to predict the treatment response of patients with non-small cell lung cancer.

Background: This study developed and validated a deep learning (DL) model based on whole slide imaging (WSI) for predicting the treatment response to chemotherapy and radiotherapy (CRT) among patients with non-small cell lung cancer (NSCLC). Methods: We collected the WSI of 120 nonsurgical patients with NSCLC treated with CRT from three hospitals in China. Based on the processed WSI, two DL models were established: a tissue classification model which was used to select tumor-tiles, and another model which predicted the treatment response of the patients based on the tumor-tiles (predicting the treatment response of each tile). A voting method was employed, by which the label of tiles with the greatest quantity from 1 patient would be used as the label of the patient. Results: The tissue classification model had a great performance (accuracy in the training set/internal validation set =0.966/0.956). Based on 181,875 tumor-tiles selected by the tissue classification model, the model for predicting the treatment response demonstrated strong predictive ability (accuracy of patient-level prediction in the internal validation set/external validation set 1/external validation set 2 =0.786/0.742/0.737). Conclusions: A DL model was constructed based on WSI to predict the treatment response of patients with NSCLC. This model can help doctors to formulate personalized CRT plans and improve treatment outcomes.

Corrigendum: Predicting the recurrence and overall survival of patients with glioma based on histopathological images using deep learning.

[This corrects the article DOI: 10.3389/fneur.2023.1100933.].

Contribution of whole slide imaging-based deep learning in the assessment of intraoperative and postoperative sections in neuropathology.

The pathological diagnosis of intracranial germinoma (IG), oligodendroglioma, and low-grade astrocytoma on intraoperative frozen section (IFS) and hematoxylin-eosin (HE)-staining section directly determines patients' treatment options, but it is a difficult task for pathologists. We aimed to investigate whether whole-slide imaging (WSI)-based deep learning can contribute new precision to the diagnosis of IG, oligodendroglioma, and low-grade astrocytoma. Two types of WSIs (500 IFSs and 832 HE-staining sections) were collected from 379 patients at multiple medical centers. Patients at Center 1 were split into the training, testing, and internal validation sets (3:1:1), while the other centers were the external validation sets. First, we subdivided WSIs into small tiles and selected tissue tiles using a tissue tile selection model. Then a tile-level classification model was established, and the majority voting method was used to determine the final diagnoses. Color jitter was applied to the tiles so that the deep learning (DL) models could adapt to the variations in the staining. Last, we investigated the effectiveness of model assistance. The internal validation accuracies of the IFS and HE models were 93.9% and 95.3%, respectively. The external validation accuracies of the IFS and HE models were 82.0% and 76.9%, respectively. Furthermore, the IFS and HE models can predict Ki-67 positive cell areas with R2 of 0.81 and 0.86, respectively. With model assistance, the IFS and HE diagnosis accuracy of pathologists improved from 54.6%-69.7% and 53.5%-83.7% to 87.9%-93.9% and 86.0%-90.7%, respectively. Both the IFS model and the HE model can differentiate the three tumors, predict the expression of Ki-67, and improve the diagnostic accuracy of pathologists. The use of our model can assist clinicians in providing patients with optimal and timely treatment options.

A multiomics approach-based prediction of radiation pneumonia in lung cancer patients: impact on survival outcome.

PURPOSE: To predict the risk of radiation pneumonitis (RP), a multiomics model was built to stratify lung cancer patients. Our study also investigated the impact of RP on survival. METHODS: This study retrospectively collected 100 RP and 99 matched non-RP lung cancer patients treated with radiotherapy from two independent centres. They were divided into training (n = 175) and validation cohorts (n = 24). The radiomics, dosiomics and clinical features were extracted from planning CT and electronic medical records and were analysed by LASSO Cox regression. A multiomics prediction model was developed by the optimal algorithm. Overall survival (OS) between the RP, non-RP, mild RP, and severe RP groups was analysed by the Kaplan‒Meier method. RESULTS: Sixteen radiomics features, two dosiomics features, and one clinical feature were selected to build the best multiomics model. The optimal performance for predicting RP was the area under the receiver operating characteristic curve (AUC) of the testing set (0.94) and validation set (0.92). The RP patients were divided into mild (≤ 2 grade) and severe (> 2 grade) RP groups. The median OS was 31 months for the non-RP group compared with 49 months for the RP group (HR = 0.53, p = 0.0022). Among the RP subgroup, the median OS was 57 months for the mild RP group and 25 months for the severe RP group (HR = 3.72, p < 0.0001). CONCLUSIONS: The multiomics model contributed to improving the accuracy of RP prediction. Compared with the non-RP patients, the RP patients displayed longer OS, especially the mild RP patients.

Predicting the recurrence and overall survival of patients with glioma based on histopathological images using deep learning.

Background: A deep learning (DL) model based on representative biopsy tissues can predict the recurrence and overall survival of patients with glioma, leading to optimized personalized medicine. This research aimed to develop a DL model based on hematoxylin-eosin (HE) stained pathological images and verify its diagnostic accuracy. Methods: Our study retrospectively collected 162 patients with glioma and randomly divided them into a training set (n = 113) and a validation set (n = 49) to build a DL model. The HE-stained slide was segmented into a size of 180 × 180 pixels without overlapping. The patch-level features were extracted by the pre-trained ResNet50 to predict the recurrence and overall survival. Additionally, a light-strategy was introduced where low-size digital biopsy images with clinical information were inputted into the DL model to ensure minimum memory occupation. Results: Our study extracted 512 histopathological features from the HE-stained slides of each glioma patient. We identified 36 and 18 features as significantly related to disease-free survival (DFS) and overall survival (OS), respectively, (P < 0.05) using the univariate Cox proportional-hazards model. Pathomics signature showed a C-index of 0.630 and 0.652 for DFS and OS prediction, respectively. The time-dependent receiver operating characteristic (ROC) curves, along with nomograms, were used to assess the diagnostic accuracy at a fixed time point. In the validation set (n = 49), the area under the curve (AUC) in the 1- and 2-year DFS was 0.955 and 0.904, respectively, and the 2-, 3-, and 5-year OS were 0.969, 0.955, and 0.960, respectively. We stratified the patients into low- and high-risk groups using the median hazard score (0.083 for DFS and-0.177 for OS) and showed significant differences between these groups (P < 0.001). Conclusion: Our results demonstrated that the DL model based on the HE-stained slides showed the predictability of recurrence and survival in patients with glioma. The results can be used to assist oncologists in selecting the optimal treatment strategy in clinical practice.

The value of postoperative radiotherapy in thymoma patients with myasthenia gravis.

INTRODUCTION: Surgery is the first-line treatment for patients with thymoma associated with myasthenia gravis (MG); however, the value of radiotherapy among these patients remains debatable. Herein, we examined the impact of postoperative radiotherapy (PORT) on the efficacy and prognosis of patients with thymoma and MG. METHODS: This retrospective cohort study included 126 patients with thymoma and MG who were enrolled in the Xiangya Hospital clinical database between 2011 and 2021. Demographic and clinical data were collected including sex, age, histologic subtype, Masaoka-Koga staging, primary tumor, lymph node, metastasis (TNM) staging, and therapeutic modalities. To evaluate short-term MG symptom improvement following PORT, we examined changes in the quantitative myasthenia gravis (QMG) scores within 3 months post-treatment. Minimal manifestation status (MMS) was the main endpoint for assessing long-term improvement in MG symptoms. Overall survival (OS) and disease-free survival (DFS) were primary endpoints to determine the impact of PORT on prognosis. RESULTS: Effects of PORT on MG symptoms: QMG scores significantly differed between the non-PORT and PORT groups (χ2 = 6.300, p = 0.012). The median time to achieve MMS was significantly shorter in the PORT group than that in the non-PORT group (2.0 years vs. 4.4 years; p = 0.031). Multivariate analysis revealed that radiotherapy was associated with a reduced time to achieve MMS (hazard ratio [HR] 1.971, 95% confidence interval [CI]:1.102-3.525, p = 0.022). Effects of PORT on DFS and OS: The 10-year OS rate of the entire cohort was 90.5%, whereas OS rates for the PORT and non-PORT groups were 94.4 and 85.1%, respectively. The 5-year DFS rates for the whole cohort, PORT group, and non-PORT group were 89.7, 95.8, and 81.5%, respectively. PORT was associated with improved DFS (HR 0.139, 95% CI: 0.037-0.533, p = 0.004). In the high-risk histologic subgroup (type B2, B3), patients who received PORT had better OS (p = 0.015) and DFS (p = 0.0053) than those who did not receive PORT. PORT was associated with improved DFS (HR 0.232, 95% CI: 0.069-0.782, p = 0.018) in Masaoka-Koga stages II, III, and IV disease. CONCLUSIONS: Overall, our findings indicate that PORT positively impacts thymoma patients with MG, particularly those with a higher histologic subtype and Masaoka-Koga staging.

Using deep learning to predict survival outcome in non-surgical cervical cancer patients based on pathological images.

PURPOSE: We analyzed clinical features and the representative HE-stained pathologic images to predict 5-year overall survival via the deep-learning approach in cervical cancer patients in order to assist oncologists in designing the optimal treatment strategies. METHODS: The research retrospectively collected 238 non-surgical cervical cancer patients treated with radiochemotherapy from 2014 to 2017. These patients were randomly divided into the training set (n = 165) and test set (n = 73). Then, we extract deep features after segmenting the HE-stained image into patches of size 224 × 224. A Lasso-Cox model was constructed with clinical data to predict 5-year OS. C-index evaluated this model performance with 95% CI, calibration curve, and ROC. RESULTS: Based on multivariate analysis, 2 of 11 clinical characteristics (C-index 0.68) and 2 of 2048 pathomic features (C-index 0.74) and clinical-pathomic model (C-index 0.83) of nomograms predict 5-year survival in the training set, respectively. In test set, compared with the pathomic and clinical characteristics used alone, the clinical-pathomic model had an AUC of 0.750 (95% CI 0.540-0.959), the clinical predictor model had an AUC of 0.729 (95% CI 0.551-0.909), and the pathomic model AUC was 0.703 (95% CI 0.487-0.919). Based on appropriate nomogram scores, we divided patients into high-risk and low-risk groups, and Kaplan-Meier survival probability curves for both groups showed statistical differences. CONCLUSION: We built a clinical-pathomic model to predict 5-year OS in non-surgical cervical cancer patients, which may be a promising method to improve the precision of personalized therapy.

Treatment planning computed tomography radiomics for predicting treatment outcomes and haematological toxicities in locally advanced cervical cancer treated with radiotherapy: A retrospective cohort study.

OBJECTIVE: We evaluated whether radiomic features extracted from planning computed tomography (CT) scans predict clinical end points in patients with locally advanced cervical cancer (LACC) undergoing intensity-modulated radiation therapy and brachytherapy. DESIGN: A retrospective cohort study. SETTING: Xiangya Hospital of Central South University, Changsha, Hunan, China. POPULATION: Two hundred and fifty-seven LACC patients who were treated with intensity-modulated radiotherapy from 2014 to 2017. METHODS: Patients were allocated into the training/validation sets (3:1 ratio) using proportional random sampling, resulting in the same proportion of groups in the two sets. We extracted 254 radiomic features from each of the gross target volume, pelvis and sacral vertebrae. The sequentially backward elimination support vector machine algorithm was used for feature selection and end point prediction. MAIN OUTCOMES AND MEASURES: Clinical end points include tumour complete response (CR), 5-year overall survival (OS), anaemia, and leucopenia. RESULTS: A combination of ten clinicopathological parameters and 34 radiomic features performed best for predicting CR (validation balanced accuracy: 80.8%). The validation balanced accuracy of 54 radiomic features was 85.8% for OS, and their scores can stratify patients into the low-risk and high-risk groups (5-year OS: 95.5% versus 36.4%, p < 0.001). The clinical and radiomic models were also predictive of anaemia and leucopenia (validation balanced accuracies: 71.0% and 69.9%). CONCLUSION: This study demonstrated that combining clinicopathological parameters with CT-based radiomics may have value for predicting clinical end points in LACC. If validated, this model may guide therapeutic strategy to optimise the effectiveness and minimise toxicity or treatment for LACC.

Case report: Brain metastasis necrosis with immune checkpoint inhibitors plus chemotherapy for advanced non-small cell lung cancer.

The emergence of immune checkpoint inhibitors (ICIs) has reshaped the landscape of advanced lung cancer treatment. The brain is the most common metastatic site for lung cancer. Whether conventional criteria can evaluate the intracranial response of ICIs remains unclear. Here, we report a well-documented case of intracranial necrosis confirmed by post-operative pathology after only one cycle of chemo-immunotherapy without any radiation therapy, which suggests that immunotherapy elicits strong anti-tumor responses for intracranial metastasis and promotes intracranial necrosis, resulting in a temporary increase in size of the target lesions. Still, the specific mechanisms and management strategies need to be further explored.

Nanoradiosensitizer with good tissue penetration and enhances oral cancer radiotherapeutic effect.

Low dose non-toxic disulfide cross-linked micelle (DCM) encapsulated paclitaxel (PTX) was found to be highly efficacious as a radiosensitizer against oral cancer preclinical model. Intensity-modulated radiation therapy was locally administered for three consecutive days 24 h after intravascular injection of DCM-[PTX] at 5 mg/kg PTX. DCM-[PTX] NPs combined with conventional radiotherapy (2 Gy) resulted in a 1.7-fold improvement in therapeutic efficacy compared to conventional PTX plus radiotherapy. Interestingly, we found that radiotherapy can decrease tight junctions and increase the accumulation of DCM-[PTX] in tumor sites. Stereotactic body radiotherapy (SBRT) given at 6 Gy was used to further investigate the synergistic anti-tumor effect. Tumor tissues were collected to analyze the relationship between the time interval after SBRT and the biodistribution of the nanomaterials. Compared to combination DCM-[PTX] with conventional radiation dose, combination DCM-PTX with SBRT was found to be more efficacious in inhibiting tumor growth.

Programmable Bispecific Nano-immunoengager That Captures T Cells and Reprograms Tumor Microenvironment.

Immune checkpoint blockade (ICB) therapy has revolutionized clinical oncology. However, the efficacy of ICB therapy is limited by the ineffective infiltration of T effector (Teff) cells to tumors and the immunosuppressive tumor microenvironment (TME). Here, we report a programmable tumor cells/Teff cells bispecific nano-immunoengager (NIE) that can circumvent these limitations to improve ICB therapy. The peptidic nanoparticles (NIE-NPs) bind tumor cell surface α3ß1 integrin and undergo in situ transformation into nanofibrillar network nanofibers (NIE-NFs). The prolonged retained nanofibrillar network at the TME captures Teff cells via the activatable α4ß1 integrin ligand and allows sustained release of resiquimod for immunomodulation. This bispecific NIE eliminates syngeneic 4T1 breast cancer and Lewis lung cancer models in mice, when given together with anti-PD-1 antibody. The in vivo structural transformation-based supramolecular bispecific NIE represents an innovative class of programmable receptor-mediated targeted immunotherapeutics to greatly enhance ICB therapy against cancers.

Diterpene glycosides, acetophenone glycosides and tannins from polar extracts of the root of Euphorbia fischeriana with cytotoxicity and antibacterial activities.

The phytochemical study on Euphorbia fischeriana, a folk medicinal plant in China, led to the isolation of eight undescribed glycosides, including two diterpene glycosides, three acetophenone glycosides and three tannins together with eight known ones. Their planar structures were elucidated by extensive analyses of 1D, 2D NMR experiments and HRESIMS. The absolute configurations were determined by NOESY experiments, ECD calculations. All undescribed compounds were evaluated for their cytotoxicity and antibacterial activities in vitro. Two diterpene glycosides (1-2) showed cytotoxic activity with IC50 values ranging from 5.4 to 16.2 µM toward Hep-G2, Hep-3B, A549, NCI-H460 and AGS cells. Tannins (6-8) showed the significant antibacterial activity with MIC values in the range of 1.56-6.25 µg/mL.

Three New Compounds from Delphinium kamaonense Hunth and Their in†Vitro Cytotoxic and Potential Antioxidant Activities.

A new amide (1), two new phenylpropanoid derivatives (2, 3), along with three new natural products, including three nitrogen chirality compounds, N-(3-methoxy-1,3-dioxopropyl)-D-phenylalanine methyl ester (4), N-(3-methoxy-1,3-dioxopropyl)-L-phenylalanine methyl ester (5), and N-acetyl-L-phenylalanine methyl ester (6), as well as dimethyl (2R,3R)-2-hydroxy-3-(((E)-3-(4-hydroxyphenyl)acryloyl)oxy)succinate (7) and dimethyl (S,E)-2-((3-(4-hydroxy-3-methoxyphenyl)acryloyl)oxy)succinate (8) were isolated from Delphinium kamaonense Hunth. Their structures were elucidated by extensive analysis of 1D and 2D NMR, and HR-ESI-MS experiments, and the absolute configurations were determined by comparative analysis of specific optical rotation. Compound 1 exhibited a moderate cytotoxicity effect against Hep-3B cancer cell lines (IC50 41.39±0.13 µM) and an excellent antioxidant activity (IC50 0.527±0.06 µM in ABTS assay, and 1.235±0.09 µM in DPPH assay, respectively), which was superior to vitamin C in ABTS (IC50 1.670±0.07 µM) and DPPH (IC50 19.10±0.40 µM) methods.

Radiomics for Predicting Response of Neoadjuvant Chemotherapy in Nasopharyngeal Carcinoma: A Systematic Review and Meta-Analysis.

Purpose: This study examined the methodological quality of radiomics to predict the effectiveness of neoadjuvant chemotherapy in nasopharyngeal carcinoma (NPC). We performed a meta-analysis of radiomics studies evaluating the bias risk and treatment response estimation. Methods: Our study was conducted through a literature review as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We included radiomics-related papers, published prior to January 31, 2022, in our analysis to examine the effectiveness of neoadjuvant chemotherapy in NPC. The methodological quality was assessed using the radiomics quality score. The intra-class correlation coefficient (ICC) was employed to evaluate inter-reader reproducibility. The pooled area under the curve (AUC), pooled sensitivity, and pooled specificity were used to assess the ability of radiomics to predict response to neoadjuvant chemotherapy in NPC. Lastly, the Quality Assessment of Diagnostic Accuracy Studies technique was used to analyze the bias risk. Results: A total of 12 studies were eligible for our systematic review, and 6 papers were included in our meta-analysis. The radiomics quality score was set from 7 to 21 (maximum score: 36). There was satisfactory ICC (ICC = 0.987, 95% CI: 0.957-0.996). The pooled sensitivity and specificity were 0.88 (95% CI: 0.71-0.95) and 0.82 (95% CI: 0.68-0.91), respectively. The overall AUC was 0.91 (95% CI: 0.88-0.93). Conclusion: Prediction response of neoadjuvant chemotherapy in NPC using machine learning and radiomics is beneficial in improving standardization and methodological quality before applying it to clinical practice.

Fatty acid oxidation fuels glioblastoma radioresistance with CD47-mediated immune evasion.

Glioblastoma multiforme (GBM) remains the top challenge to radiotherapy with only 25% one-year survival after diagnosis. Here, we reveal that co-enhancement of mitochondrial fatty acid oxidation (FAO) enzymes (CPT1A, CPT2 and ACAD9) and immune checkpoint CD47 is dominant in recurrent GBM patients with poor prognosis. A glycolysis-to-FAO metabolic rewiring is associated with CD47 anti-phagocytosis in radioresistant GBM cells and regrown GBM after radiation in syngeneic mice. Inhibition of FAO by CPT1 inhibitor etomoxir or CRISPR-generated CPT1A-/-, CPT2-/-, ACAD9-/- cells demonstrate that FAO-derived acetyl-CoA upregulates CD47 transcription via NF-κB/RelA acetylation. Blocking FAO impairs tumor growth and reduces CD47 anti-phagocytosis. Etomoxir combined with anti-CD47 antibody synergizes radiation control of regrown tumors with boosted macrophage phagocytosis. These results demonstrate that enhanced fat acid metabolism promotes aggressive growth of GBM with CD47-mediated immune evasion. The FAO-CD47 axis may be targeted to improve GBM control by eliminating the radioresistant phagocytosis-proofing tumor cells in GBM radioimmunotherapy.