Search

Rationale and Objectives Histological subtypes of lung cancers are critical for clinical treatment decision. In this study, we attempt to use 3D deep learning and radiomics methods to automatically distinguish lung adenocarcinomas (ADC), squamous cell carcinomas (SCC), and small cell lung cancers (SCLC) respectively on Computed Tomography images, and then compare their performance. Materials and Methods 920 patients (mean age 61.2, range, 17–87; 340 Female and 580 Male) with lung cancer, including 554 patients with ADC, 175 patients with lung SCC and 191 patients with SCLC, were included in this retrospective study from January 2013 to August 2018. Histopathologic analysis was available for every patient. The classification models based on 3D deep learning (named the ProNet) and radiomics (named com_radNet) were designed to classify lung cancers into the three types mentioned above according to histopathologic results. The training, validation and testing cohorts counted 0.70, 0.15, and 0.15 of the whole datasets respectively. Results The ProNet model used to classify the three types of lung cancers achieved the F1-scores of 90.0%, 72.4%, 83.7% in ADC, SCC, and SCLC respectively, and the weighted average F1-score of 73.2%. For com_radNet, the F1-scores achieved 83.1%, 75.4%, 85.1% in ADC, SCC, and SCLC, and the weighted average F1-score was 72.2%. The area under the receiver operating characteristic curve of the ProNet model and com_radNet were 0.840 and 0.789, and the accuracy were 71.6% and 74.7% respectively. Conclusion The ProNet and com_radNet models we developed can achieve high performance in distinguishing ADC, SCC, and SCLC and may be promising approaches for non-invasive predicting histological subtypes of lung cancers.

BACKGROUND Primary central nervous system diffuse large B-cell lymphoma (PCNS-DLBCL) is a relatively rare and aggressive neoplasm. High-dose methotrexate (HD-MTX) is an effective regimen for the treatment of PCNS-DLBCL, but MTX-related toxicity remains a problem. The aim of this analysis study was to investigate the influence of the methylenetetrahydrofolate reductase (MTHFR) gene C677T polymorphism on HD-MTX-related toxicity in patients with PCNS-DLBCL. MATERIAL/METHODS A prospective, observational study was conducted to analyze 148 MTX courses in 32 patients with PCNS-DLBCL. RESULTS The delayed MTX clearance was observed in 53 cycles (35.8%). The patients carrying the homozygous variant genotype had a higher risk of developing nephrotoxicity than those carrying the wild-type genotype (odds ratio [OR] 13.08; 95% confidence interval [CI], 1.65-103.86; P = .002) or heterozygous variant genotype (OR 8.43; 95% CI, 2.31-30.70; P < .001). Significant differences were observed in hepatotoxicity (OR 9.33; 95% CI, 2.54-34.27; P < .001) and hematologic toxicity (OR 3.09; 95% CI, 1.18-8.07; P = .024) in addition to nephrotoxicity between the homozygous variant genotype and the wild-type genotype. CONCLUSION The homozygous mutation of C to T at nucleotide 677 increases the risk on HD-MTX-related toxicity. The MTHFR C677T polymorphism can be used to predict HD-MTX-related toxicity for patients with PCNS-DLBCL.

Rationale and Objectives To explore the potential value of radiomic features-derived approach in assessing PD-L1 expression status in nonsmall cell lung cancer (NSCLC) patients. Materials and Methods A cohort of 399 stage I–IV NSCLC patients were enrolled. Tumor segmentation was performed to select essential primary lesions of NSCLC cases after PET/CT images acquisition. Features were extracted, then filtered with automatic relevance determination and minimized with LASSO model based on its relevance of PD-L1 expression status. Finally, we built predictive models with features from the CT, the PET, and the PET/CT images, respectively, for differentiating different status of specific PD-L1 types. Five-fold cross validation was practiced to evaluate the signatures’ accuracy, and the receiver operating characteristic as well as the corresponding area under the curve (AUC) was reckoned for each model. Results With the total of 24 selected features which were significantly associated with PD-L1 expression levels, models based on CT-, PET-, PET/CT-derived features were built and compared. For PD-L1 (SP142) expression level over 1% prediction, models that comprised radiomic features from the CT, the PET, and the PET/CT images resulted in an AUC of 0.97, 0.61, and 0.97, respectively; models for over 50% prediction resulted with AUC of 0.80, 0.65, and 0.77. For PD-L1 (28-8) expression level prediction, predictive models of over 1% expression scored at 0.86, 0.62, and 0.85; and signatures of over 50% expression reached the score of AUCs at 0.91, 0.75, and 0.88, respectively. Conclusion The radiomic-based predictive approach, especially CT-derived predictive model, may anticipate PD-L1 expression status in NSCLC patients relatively accurate. It may be helpful in guiding immunotherapy in clinical practice and deserves further analysis.

Immunotherapy targeting glucocorticoid-induced TNFR-related protein (GITR) exhibited strong anti-tumor capacity in mouse model but poor efficacy in clinical trials. This may be attributed to the different GITR expression mode between human and mouse. In this study, we analyzed single-cell RNA sequencing (scRNA-seq) data of human gastric cancer (GC) and used flow to explore the GITR expression across T cell subsets and tissue types in GC patients. We revealed that GITR+ CD4 T cells, including regulatory CD4 T (Treg) cells and conventional CD4 T (Tconv) cells, might contribute to the immunosuppressive microenvironment in GC. The enrichment of these cells was associated with a worse prognosis. Moreover, we found the cellular distribution of GITR protein in Treg cells was microenvironment dependent. In conclusion, GITR is still an important immune checkpoint need to be studied.

To evaluate Cytokeratin 19 expression in hepatocellular carcinoma (HCC) with diffusion parameters derived from mono-exponential model (MEM), stretched exponential model (SEM), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM) imaging, and fractional order calculus (FROC) model and compare their predictive performance.61 patients with pathologically confirmed primary HCC were included in this prospective study. All the DWIs were acquired using a 3.0 T MR scanner with 10b-values (0-2000 s/mmADC, DDC, DADC, DDC, D

In the present study, we report our recently developed new approach to inducing antigen-specific immune response. We use two nucleophilic substitution "click" chemistry processes to successfully couple protein antigens or peptides to mouse spleen cells or T cells by a heterobifunctional crosslinker, succinimidyl-4-(N-maleimidomethyl cyclohexane)-1-carboxylate (SMCC) or sulfo-SMCC. SMCC and its water-soluble analog sulfo-SMCC contain N-hydroxysuccinimide (NHS) ester and maleimide groups, which allow stable covalent conjugation of amine- and sulfhydryl-containing molecules in trans. Protein coupling to cells relies on the free sulfhydryls (thiols) on cell surfaces and the free amines on protein antigens. Although the amount of protein coupled to cells is limited due to the limited number of cell surface thiols, the injection of spleen cells coupled with antigenic proteins, such as keyhole limpet hemocyanin (KLH) or ovalbumin (OVA), induces a potent antigen-specific immune response in vivo, which is even stronger than that induced by the injection of a large dose of protein plus adjuvants. In addition, short peptides coupled to purified splenic T cells also potently elicit peptide-specific T cell proliferation in vivo after injection. Further studies show that antigen-coupled spleen cell treatment leads to augmented IFN-γ-producing T cells. Our study provides a unique antigen delivery method that efficiently distributes antigen to the entire immune system, subsequently eliciting a potent antigen-specific immune response with enhanced IFN-γ production. The findings in the present study suggest that this antigen-cell coupling strategy could be employed in immunotherapy for cancers, infectious diseases as well as immune-mediated disorders.