Esophageal squamous cell cancer (ESCC) is a high incidence and mortality disease worldwide. However, specificity and sensitivity of its diagnostic and prognostic biomarkers are still unsatisfactory. Recently, circular RNAs (circRNAs) as biomarkers have been studied extensively while the expression profile and clinical significance of circRNAs in ESCC have rarely been studied. We performed circular RNA microarray in 3 pairs of ESCC frozen tumor and non-tumor tissues to identify ESCC-related circRNAs and found 1045 up-regulated and 1032 down-regulated circRNAs among which 6 circRNAs (hsa_circ_0062459, hsa_circ_0076535, hsa_circ_0072215, hsa_circ_0042261, hsa_circ_0001946, and hsa_circ_0043603) displayed consistency with microarray results by qRT-PCR. 3 circRNAs (hsa_circ_0062459, hsa_circ_0001946, and hsa_circ_0043603) were also detected in plasma and 2 of them except hsa_circ_0062459 could be used as diagnostic biomarkers and found in exosome of cell-conditioned culture conditioned media. The AUC, sensitivity and specificity of hsa_circ_0001946 were 0.894, 92, 80%, of hsa_circ_0043603 were 0.836, 64, 92% while a signature combining them were 0.928, 84 and 98%. Hsa_circ_0001946 was confirmed to predict the recurrence, overall survival (OS) and disease-free survival (DFS) in frozen and FFPE tissues, while its overexpression decreased cell proliferation, migration, and invasion.

Background: To predict pathological complete response (pCR) in patients receiving neoadjuvant immunochemotherapy (nICT) for esophageal squamous cell carcinoma (ESCC), we explored the factors that influence pCR after nICT and established a combined nomogram model. Methods: We retrospectively included 164 ESCC patients treated with nICT. The radiomics signature and hematology model were constructed utilizing least absolute shrinkage and selection operator (LASSO) regression, and the radiomics score (radScore) and hematology score (hemScore) were determined for each patient. Using the radScore, hemScore, and independent influencing factors obtained through univariate and multivariate analyses, a combined nomogram was established. The consistency and prediction ability of the nomogram were assessed utilizing calibration curve and the area under the receiver operating factor curve (AUC), and the clinical benefits were assessed utilizing decision curve analysis (DCA). Results: We constructed three predictive models.The AUC values of the radiomics signature and hematology model reached 0.874 (95% CI: 0.819–0.928) and 0.772 (95% CI: 0.699–0.845), respectively. Tumor length, cN stage, the radScore, and the hemScore were found to be independent factors influencing pCR according to univariate and multivariate analyses (P < 0.05). A combined nomogram was constructed from these factors, and AUC reached 0.934 (95% CI: 0.896–0.972). DCA demonstrated that the clinical benefits brought by the nomogram for patients across an extensive range were greater than those of other individual models. Conclusions: By combining CT radiomics, hematological factors, and clinicopathological characteristics before treatment, we developed a nomogram model that effectively predicted whether ESCC patients would achieve pCR after nICT, thus identifying patients who are sensitive to nICT and assisting in clinical treatment decision-making.

Glioma refers to a highly prevalent type of brain tumor that is strongly associated with a high mortality rate. During the treatment process of the disease, it is particularly important to accurately perform segmentation of the glioma from Magnetic Resonance Imaging (MRI). However, existing methods used for glioma segmentation usually rely solely on either local or global features and perform poorly in terms of capturing and exploiting critical information from tumor volume features. Herein, we propose a local and global dual transformer with an attentional supervision U-shape network called DTASUnet, which is purposed for glioma segmentation. First, we built a pyramid hierarchical encoder based on 3D shift local and global transformers to effectively extract the features and relationships of different tumor regions. We also designed a 3D channel and spatial attention supervision module to guide the network, allowing it to capture key information in volumetric features more accurately during the training process. In the BraTS 2018 validation set, the average Dice scores of DTASUnet for the tumor core (TC), whole tumor (WT), and enhancing tumor (ET) regions were 0.845, 0.905, and 0.808, respectively. These results demonstrate that DTASUnet has utility in assisting clinicians with determining the location of gliomas to facilitate more efficient and accurate brain surgery and diagnosis.

Aim: The accurate reconstruction of cone-beam computed tomography (CBCT) from sparse projections is one of the most important areas for study. The compressed sensing theory has been widely employed in the sparse reconstruction of CBCT. However, the total variation (TV) approach solely uses information from the i-coordinate, j-coordinate, and k-coordinate gradients to reconstruct the CBCT image.Materials and Methods: It is well recognized that the CBCT image can be reconstructed more accurately with more gradient information from different directions. Thus, this study introduces a novel approach, named the new multi-gradient direction total variation minimization method. The method uses gradient information from the ij-coordinate, ik-coordinate, and jk-coordinate directions to reconstruct CBCT images, which incorporates nine different types of gradient information from nine directions.Results: This study assessed the efficacy of the proposed methodology using under-sampled projections from four different experiments, including two digital phantoms, one patient’s head dataset, and one physical phantom dataset. The results indicated that the proposed method achieved the lowest RMSE index and the highest SSIM index. Meanwhile, we compared the voxel intensity curves of the reconstructed images to assess the edge structure preservation. Among the various methods compared, the curves generated by the proposed method exhibited the highest level of consistency with the gold standard image curves.Conclusion: In summary, the proposed method showed significant potential in enhancing the quality and accuracy of CBCT image reconstruction.

Anastomotic leak (AL) is a common complication in patients with operable esophageal squamous cell carcinoma (ESCC) treated with neoadjuvant chemoradiotherapy (NCRT) and radical esophagectomy. Therefore, this study aimed to establish and validate a nomogram to predict the occurrence of AL. Between March 2016 and December 2022, ESCC patients undergoing NCRT and radical esophagectomy were retrospectively collected in China. Clinicopathologic and radiomics characteristics were included in the univariate logistic regression analysis, and statistically significant factors were enrolled to develop the nomogram, which was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). 231 eligible patients were divided into training (n = 159) and validation cohorts (n = 72). Univariate and multivariate analyses revealed that dose at the anastomosis ≥ 24 Gy, gross tumor volume ≥ 60 cm3, postoperative albumin < 35 g/L, comorbidities, duration of surgery ≥ 270 min, and computed tomography-based radiomics characteristics were independent predictors of AL. The nomogram AUC in the training and validation cohorts was 0.845 (95% confidence interval [CI]: 0.770–0.920) and 0.839 (95% CI: 0.718–0.960), respectively, indicating good discriminatory ability. The calibration curves showed good agreement between the predicted and actual AL occurrence and the DCA demonstrated favorable clinical outcomes. We developed and validated a nomogram based on radiomics and clinicopathologic characteristics. This predictive model could be a powerful tool to predict AL occurrence in patients with ESCC treated with NCRT.

Background: Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI)-based combination therapy modalities for patients with EGFR-mutated stage IV non-small cell lung cancer (NSCLC) are being investigated. We evaluated the value and safety of third-generation EGFR-TKIs combined with thoracic radiotherapy (TRT) in patients with oligo-organ metastatic, along with the optimal TRT timing and dose. Methods: We retrospectively enrolled patients with EGFR-mutated oligo-organ metastatic NSCLC who received first-line third-generation EGFR-TKIs from 2018 to 2023. Patients were divided into TKI-alone and TKI+TRT groups according to whether TRT was added. Propensity score matching (PSM) was implemented to decrease bias. The primary endpoint was progression-free survival (PFS). Secondary endpoints included overall survival (OS) and safety. Results: A total of 236 patients were included, the median follow-up was 28.4 months. After PSM, baseline characteristics were balanced between the TKI+TRT (n=69) and TKI-alone (n=108) groups. Compared with the TKI-alone group, the TKI+TRT group presented significantly better PFS (28.6 vs. 19.8 months, hazard ratio [HR]=0.48, P = 0.00024) and OS (42.2 vs. 35.1 months, HR = 0.54, P = 0.039). In patients who received TRT, the tumor shrinkage group demonstrated significantly improved PFS (HR = 0.36, P = 0.0035) and OS (HR = 0.13, P = 0.0012) compared to the tumor enlargement/Stabilization group. The high-dose group showed superior PFS (HR = 0.43, P = 0.011) and OS (HR = 0.36, P = 0.023) compared to the low-dose group. Only 5.8% of patients in the TKI+TRT group developed grade ≥3 pneumonitis. Conclusion: Incorporating TRT provided significant survival benefits in patients with oligo-organ metastatic NSCLC who received first-line third-generation EGFR TKIs, with acceptable side effects. The administration of higher radiation doses during a phase of tumor shrinkage may be associated with optimal outcomes.