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BackgroundStudies have shown that magnetic resonance imaging (MRI)‐based deep learning radiomics (DLR) has the potential to assess glioma grade; however, its role in predicting telomerase reverse transcriptase (TERT) promoter mutation status in patients with glioblastoma (GBM) remains unclear.PurposeTo evaluate the value of deep learning (DL) in multiparametric MRI‐based radiomics in identifying TERT promoter mutations in patients with GBM preoperatively.Study TypeRetrospective.PopulationA total of 274 patients with isocitrate dehydrogenase‐wildtype GBM were included in the study. The training and external validation cohorts included 156 (54.3 ± 12.7 years; 96 males) and 118 (54 .2 ± 13.4 years; 73 males) patients, respectively.Field Strength/SequenceAxial contrast‐enhanced T1‐weighted spin‐echo inversion recovery sequence (T1CE), T1‐weighted spin‐echo inversion recovery sequence (T1WI), and T2‐weighted spin‐echo inversion recovery sequence (T2WI) on 1.5‐T and 3.0‐T scanners were used in this study.AssessmentOverall tumor area regions (the tumor core and edema) were segmented, and the radiomics and DL features were extracted from preprocessed multiparameter preoperative brain MRI images—T1WI, T1CE, and T2WI. A model based on the DLR signature, clinical signature, and clinical DLR (CDLR) nomogram was developed and validated to identify TERT promoter mutation status.Statistical TestsThe Mann–Whitney U test, Pearson test, least absolute shrinkage and selection operator, and logistic regression analysis were applied for feature selection and construction of radiomics and DL signatures. Results were considered statistically significant at P‐value <0.05.ResultsThe DLR signature showed the best discriminative power for predicting TERT promoter mutations, yielding an AUC of 0.990 and 0.890 in the training and external validation cohorts, respectively. Furthermore, the DLR signature outperformed CDLR nomogram (P = 0.670) and significantly outperformed clinical models in the validation cohort.Data ConclusionThe multiparameter MRI‐based DLR signature exhibited a promising performance for the assessment of TERT promoter mutations in patients with GBM, which could provide information for individualized treatment.Level of Evidence3Technical EfficacyStage 2

Lung cancer is a malignancy with the highest incidence of morbidity and mortality worldwide. The lack of effective detection methods leads to the ineffectiveness of convetional therapy. The aim of the current study was to analyze the hydrothorax epidermal growth factor receptor (EGFR) mutation in patients with advanced non-small lung cancer (NSCLC) and malignant pleural effusion. A new method for clinical treatment was developed through a comparison of the difference of EGFR tyrosine kinase inhibitor (EGFR-TKI)-targeted therapy. Between January 2013 and January 2015, 68 cases diagnosed with advanced non-small lung cancer and malignant pleural effusion, were enrolled in the study. Previous first-line chemotherapeutic treatment schemes had been unsuccessful. EGFR 19 and EGFR 21 sites were detected for all the patients. Platinum-based drugs were provided for patients with wild-type EGFR. These patients served as the control group and underwent four cycles of treatments, with each cycle lasting 3 weeks. TKI medicine Gefitinib (Iressa™) was administered to patients with mutant EGFR tid, po, for a duration of 4–8 months. These patients served as the experimental group. There were 41 cases of EGFR mutations, of which 13 cases had EGFR 19 site mutations, 16 cases EGFR 21 site mutations, and the remaining 12 cases had 2 site mutations. EGFR mutations were not significant for gender, age, tumor type, stage and diameter (P>0.05). The results showed that the six-month survival rate, progression-free survival time (PFS), objective response rate (RP) and disease control rate (DCR) in the experimental group were higher than those in the control group. The drug side-effects in the experimental group indicated no statistical differences compared to the control group (P>0.05). The incidence of EGFR mutation was higher in patients with advanced non-small lung cancer and malignant pleural effusion. Targeted therapy improved the survival rate and was deemed to be a safe and effective method for patients with EGFR mutations.

Purpose: To evaluate the value of intra- and peritumoral deep learning (DL) features based on multi-parametric magnetic resonance imaging (MRI) for identifying telomerase reverse transcriptase (TERT) promoter mutation in glioblastoma (GBM). Methods: In this study, we included 229 patients with GBM who underwent preoperative MRI in two hospitals between November 2016 and September 2022. We used four 2D Convolutional Neural Networks (GoogLeNet, DenseNet121, VGG16, and MobileNetV3-Large) to extract intra- and peritumoral DL features. The Mann–Whitney U test, Pearson correlation analysis, least absolute shrinkage and selection operator, and logistic regression analysis were used for feature selection and construction of DL radiomics (DLR) signatures in different regions. These multi-parametric and multi-region signatures were combined to identify TERT promoter mutation. The area under the receiver operating characteristic curve (AUC) was used to evaluate the effects of the signatures. Results: The signatures based on the DL features from the peritumoral regions with expansion distances of 2 mm, 8 mm, and 10 mm using the GoogLeNet architecture correlated with the optimal AUC values (test set: .823, .753, and .768) in the T2-weighted, T1-weighted contrast-enhanced, and T1-weighted images. Using the stacking fusion method, DLR with multi-parameter and multi-region fusion achieved the best discrimination with AUC values of .948 and .902 in the training and test sets, respectively. Conclusions: The radiomics model based on the fusion of multi-parameter MRI intra- and peritumoral DLR signatures may help to identify TERT promoter mutation in patients with GBM.