Computed Tomography-Based Intratumor Heterogeneity Predicts Response to Immunotherapy Plus Chemotherapy in Esophageal Squamous Cell Carcinoma.

RATIONALE AND OBJECTIVES: This study explored the intratumor heterogeneity (ITH) of esophageal squamous cell carcinoma (ESCC) using computed tomography (CT) and investigated the value of CT-based ITH in predicting the response to immune checkpoint inhibitor (ICI) plus chemotherapy in patients with ESCC. MATERIALS AND METHODS: This retrospective study included 416 patients with ESCC who received ICI plus chemotherapy at two independent hospitals between January 2019 and July 2022. Multiparametric CT features were extracted from ESCC lesions and screened using hierarchical clustering and dimensionality reduction algorithms. Logistic regression and machine learning models based on selected features were developed to predict treatment response and validated in separate datasets. ITH was quantified using the score calculated by the best-performing model and visualized through feature clustering and feature contribution heatmaps. A gene set enrichment analysis (GSEA) was performed to identify the biological pathways underlying the CT-based ITH. RESULTS: The extreme gradient boosting model based on CT-derived ITH had higher discriminative power, with areas under the receiver operating characteristic curve of 0.864 (95% confidence interval [CI]: 0.774-0.954) and 0.796 (95% CI: 0.698-0.893) in the internal and external validation sets. The CT-based ITH pattern differed significantly between responding and non-responding patients. The GSEA indicated that CT-based ITH was associated with immunity-, keratinization-, and epidermal cell differentiation-related pathways. CONCLUSION: CT-based ITH is an effective biomarker for identifying patients with ESCC who could benefit from ICI plus chemotherapy. Immunity-, keratinization-, and epidermal cell differentiation-related pathways may influence the patient's response to ICI plus chemotherapy.

CT-Measured Angulation Between the Frontal Bone and Bony Nasolacrimal Duct: Variations in Obstructed and Healthy Lacrimal Ducts.

PURPOSE: The aim of this study was to analyze the characteristics of CT-measured intersection angle (FB-BNLD) between the frontal bone and bony nasolacrimal duct and to provide suggestions for treating primary acquired nasolacrimal duct obstruction (PANDO) patients in West China. METHODS: Three hundred and nine participants' CT were, respectively, evaluated with RadiAnt DICOM Viewer. We defined the FB-BNLD angle >0° as the anterior type and the FB-BNLD angle ≤0° as the posterior type. RESULTS: The mean FB-BNLD was -2.52° (95% CI, -3.16° to -1.88°) across all participants, of whom 37.2% were of the anterior type and 62.8% of the posterior type. Approximately 65.0% of the female patients had a posterior FB-BNLD type, and 54.2% of the male patients had an anterior FB-BNLD type (p = .002). Posterior FB-BNLD was the dominant type in the PANDO and control groups (p = .011), and the angle of FB-BNLD was statistically different in both groups (PANDO group, -2.54° to -0.71°; control group, -4.42° to -2.67°; p < .001). Among the male participants, the type of FB-BNLD differed between the two groups (p = .036), with differences in the angle of FB-BNLD (PANDO group, 0.59° to 5.13°; control group, -4.08° to 1.89°; p = .034). There was no difference in the type of FB-BNLD in female participants between the two groups (p = .051). CONCLUSION: The present study revealed individual differences in the type of FB-BNLD, with anterior-type majority in males and posterior-type dominance in females. Evaluating the FB-BNLD type on CT can provide a fast method for knowing the nasolacrimal duct condition during planning for lacrimal manipulation.

Tumor Multiregional Mean Apparent Propagator (MAP) Features in Evaluating Gliomas-A Comparative Study With Diffusion Kurtosis Imaging (DKI).

BACKGROUND: Glioma classification affects treatment and prognosis. Reliable imaging methods for preoperatively evaluating gliomas are essential. PURPOSE: To evaluate tumor multiregional mean apparent propagator (MAP) features in glioma diagnosis and to compare those with diffusion-kurtosis imaging (DKI). STUDY TYPE: Retrospective study. SUBJECTS: 70 untreated glioma patients (31 LGGs (low-grade gliomas), 34 women; mean age, 47 ± 12 years, training (60%, n = 42) and testing cohorts (40%, n = 28)). FIELD STRENGTH/SEQUENCE: 3-T, diffusion-MRI using q-space Cartesian grid sampling with 11 different b-values. ASSESSMENT: Tumor multiregional MAP (mean squared displacement (MSD); q-space inverse variance (QIV); non-Gaussianity (NG); axial/radial non-Gaussianity (NGAx, NGRad); return-to-origin/axis/plane probability (RTOP, RTAP, and RTPP)); and DKI metrics (axial/mean/radial kurtosis (AK, MK, and RK)) on tumor parenchyma (TP) and peritumoral areas (PT) in histopathologically gliomas grading and genotyping were assessed. STATISTICAL TESTS: Mann-Whitney U; Kruskal-Wallis; Benjamini-Hochberg; Bonferroni-correction; receiver operating curve (ROC) and area under curve (AUC); DeLong's test; Random Forest (RF). P value<0.05 was considered statistically significant after multiple comparisons correction. RESULTS: Compared with LGGs, MSD, and QIV were significantly lower in TP, whereas NG, NGAx, NGRad, RTOP, RTAP, RTPP, and DKI metrics were significantly higher in HGGs (high-grade gliomas) (P ≤ 0.007), as well as in isocitrate-dehydrogenase (IDH)-mutated than IDH-wildtype gliomas (P ≤ 0.039). These trends were reversed for PT (tumor grades, P ≤ 0.011; IDH-mutation status, P ≤ 0.012). ROC analysis showed that, in TP, DKI metrics performed best in TP (AUC 0.83), whereas in PT, RTPP performed best (AUC 0.77) in glioma grading. AK performed best in TP (AUC 0.77), whereas MSD and RTPP performed best in PT (AUC 0.73) in IDH genotyping. Further RF analysis with DKI and MAP demonstrated good performance in grading (AUC 0.91, Accuracy 82%) and IDH genotyping (AUC 0.87, Accuracy 79%). DATA CONCLUSION: Tumor multiregional MAP features could effectively evaluate gliomas. The performance of MAP may be similar to DKI in TP, while in PT, MAP may outperform DKI. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography.

PURPOSE: The treatment response to initial conventional transarterial chemoembolization (cTACE) is essential for the prognosis of patients with hepatocellular carcinoma (HCC). This study explored and verified the feasibility of machine-learning models based on clinical data and contrast-enhanced computed tomography (CT) image findings to predict early responses of HCC patients after initial cTACE treatment. PATIENTS AND METHODS: Overall, 110 consecutive unresectable HCC patients who were treated with cTACE for the first time were retrospectively enrolled. Clinical data and imaging features based on contrast-enhanced CT were collected for the selection of characteristics. Treatment responses were evaluated based on the modified Response Evaluation Criteria in Solid Tumors (mRECIST) by postoperative CT examination within 2 months after the procedure. Python (version 3.70) was used to develop machine learning models. Least absolute shrinkage and selection operator (LASSO) algorithm was applied to select features with the impact on predicting treatment response after the first TACE procedure. Six machine learning algorithms were used to build predictive models, including XGBoost, decision tree, support vector machine, random forest, k-nearest neighbor, and fully convolutional networks, and their performances were compared using receiver operator characteristic (ROC) curves to determine the best performing model. RESULTS: Following TACE, 31 patients (28.2%) were described as responsive to TACE, while 72 patients (71.8%) were nonresponsive to TACE. Portal vein tumor thrombosis type, albumin level, and distribution of tumors within the liver were selected for predictive model building. Among the models, the RF model showed the best performance, with area under the curve (AUC), accuracy, sensitivity, and specificity of 0.802, 0.784, 0.904, and 0.480, respectively. CONCLUSION: Machine learning models can provide an accurate prediction of the early response of initial TACE treatment for HCC, which can help in individualizing clinical decision-making and modification of further treatment strategies for patients with unresectable HCC.

TRIM37 overexpression is associated with chemoresistance in hepatocellular carcinoma via activating the AKT signaling pathway.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver cancer in the worldwide. Sorafenib is approved for first-line therapy against advanced HCC, but chemo-resistance is still a leading cause of tumor relapse and treatment failure in HCC. Thus, there is a significant clinical need to identify effective strategies to overcome drug resistance on the disease. METHODS: The protein and mRNA expression of TRIM37 in HCC cell lines and patient tissues were determined using Real-time PCR and Western blot, respectively. HCC tissue samples were analyzed by IHC to investigate the association between TRIM37expression and the clinicopathological characteristics of HCC patients. Functional assays, such as MTT, FACS, and Tunel assay, are used to determine the oncogenic role of TRIM37 in human HCC progression. Furthermore, western blotting and luciferase assay were used to determine the mechanism of TRIM37promotes chemoresistance in HCC. RESULTS: We found that both the mRNA and protein expression of TRIM37 was markedly upregulated in HCC cell lines and tissues, especially in Sorafenib-resistance HCC tissues. Moreover, high TRIM37 expression was associated with poor prognosis with HCC patients. TRIM37 overexpression confers Sorafenib resistance on HCC cells; however, inhibition of TRIM37 sensitized HCC cell lines to Sorafenib cytotoxicity. Additionally, TRIM37 upregulated the levels of AKT activity and phosphorylated AKT, thereby activating canonical AKT signaling. CONCLUSION: Our findings suggest that targeting TRIM37 signaling may represent a promising strategy to enhance Sorafenib response in HCC patients with chemoresistant.