Zhihao Li

Dihydroouabain, a novel radiosensitizer for cervical cancer identified by automated high-throughput screening

Radiotherapy plays a crucial role in the treatment of cervical cancer, but existing radiosensitizers have limited efficacy in clinical applications. The aims of this study were to establish and verify an efficient method for identifying new radiosensitizers, to use this to identify candidate radiosensitizers for cervical cancer, and to investigate the specific mechanisms of these when used in combination with radiotherapy. An automated platform for identifying radiosensitizers for cervical cancer was created based on high-throughput screening technology. The radiosensitizing effects of candidate compounds from the LOPAC1280 chemical library were evaluated in radiosensitive and radioresistant cervical cancer cells using a clonogenic survival assay, with cell cycle analyses, and western blot analyses performed for both cell lines. The automated high-throughput screening approach identified four hit compounds. One of the most potent candidates was dihydroouabain (DHO), an inhibitor of Na DHO is a novel radiosensitizer for the treatment of cervical cancer. The automated high-throughput screening platform developed in this study proved to be powerful and effective, with the potential to be widely used in the future identification of radiosensitizers.

Preoperative prediction of lymphovascular space invasion in endometrioid adenocarcinoma: an MRI-based radiomics nomogram with consideration of the peritumoral region

Background Lymphovascular space invasion (LVSI) of endometrial cancer (EC) is a postoperative histological index, which is associated with lymph node metastases. A preoperative acknowledgement of LVSI status might aid in treatment decision-making. Purpose To explore the utility of multiparameter magnetic resonance imaging (MRI) and radiomic features obtained from intratumoral and peritumoral regions for predicting LVSI in endometrioid adenocarcinoma (EEA). Material and Methods A total of 334 EEA tumors were retrospectively analyzed. Axial T2-weighted (T2W) imaging and apparent diffusion coefficient (ADC) mapping were conducted. Intratumoral and peritumoral regions were manually annotated as the volumes of interest (VOIs). A support vector machine was applied to train the prediction models. Multivariate logistic regression analysis was used to develop a nomogram based on clinical and tumor morphological parameters and the radiomics score (RadScore). The predictive performance of the nomogram was assessed by the area under the receiver operator characteristic curve (AUC) in the training and validation cohorts. Results Among the features obtained from different imaging modalities (T2W imaging and ADC mapping) and VOIs, the RadScore had the best performance in predicting LVSI classification (AUC train  = 0.919, and AUC validation  = 0.902). The nomogram based on age, CA125, maximum anteroposterior tumor diameter on sagittal T2W images, tumor area ratio, and RadScore was established to predict LVSI had AUC values in the training and validation cohorts of 0.962 (sensitivity 94.0%, specificity 86.0%) and 0.965 (sensitivity 90.0%, specificity 85.3%), respectively. Conclusion The intratumoral and peritumoral imaging features were complementary, and the MRI-based radiomics nomogram might serve as a non-invasive biomarker to preoperatively predict LVSI in patients with EEA.