Purpose To classify radiation necrosis versus recurrence in glioma patients using a radiomics model based on combinational features and multimodality MRI images. Methods Fifty-one glioma patients who underwent radiation treatments after surgery were enrolled in this study. Sixteen patients revealed radiation necrosis while 35 patients showed tumor recurrence during the follow-up period. After treatment, all patients underwent T1-weighted, T1-weighted postcontrast, T2-weighted, and fluid-attenuated inversion recovery scans. A total of 41,284 handcrafted and 24,576 deep features were extracted for each patient. The 0.623 + bootstrap method and the area under the curve (denoted as 0.632 + bootstrap AUC) metric were used to select the features. The stepwise forward method was applied to construct 10 logistic regression models based on different combinations of image features. Results For handcrafted features on multimodality MRI, model 7 with seven features yielded the highest AUC of 0.9624, sensitivity of 0.8497, and specificity of 0.9083 in the validation set. These values were higher than the accuracy of using handcrafted features on single-modality MRI (paired t-test, p < 0.05, except sensitivity). For combined handcrafted and AlexNet features on multimodality MRI, model 6 with six features achieved the highest AUC of 0.9982, sensitivity of 0.9941, and specificity of 0.9755 in the validation set. These values were higher than the accuracy of using handcrafted features on multimodality MRI (paired t-test, p < 0.05). Conclusions Handcrafted and deep features extracted from multimodality MRI images reflecting the heterogeneity of gliomas can provide useful information for glioma necrosis/recurrence classification.

Background: Neoadjuvant radiotherapy is a commonly used method for the current standard-of-care for most patients with rectal cancer, when the effects of radioresistance are limited. The phosphatidylinositol transfer protein, cytoplasmic 1 (PITPNC1), a lipid-metabolism-related gene, has previously been proved to manifest pro-cancer effects in multiple types of cancer. However, whether PITPNC1 plays a role for developing radioresistance in rectal cancer patients is still unknown. Therefore, this study aims to investigate the role of PITPNC1 in rectal cancer radioresistance.Methods: Patient-derived tissue were used to detect the difference in the expression level of PITPNC1 between radioresistant and radiosensitive patients. Bioinformatic analyses of high-throughput gene expression data were applied to uncover the correlations between PITPNC1 level and oxidative stress. Two rectal cancer cell lines, SW620, and HCT116, were selected in vitro to investigate the effect of PITPNC1 on radioresistance, reactive oxygen species (ROS) generation, apoptosis, and proliferation in rectal cancer.Results: PITPNC1 is highly expressed in radioresistant patient-derived rectal cancer tissues compared to radiosensitive tissue; therefore, PITPNC1 inhibits the generation of ROS and improves the extent of radioresistance of rectal cancer cell lines and then inhibits apoptosis. Knocking down PITPNC1 facilitates the production of ROS while application of the ROS scavenger, N-acetyl-L-cysteine (NAC), could reverse this effect.Conclusions: PITPNC1 fuels radioresistance of rectal cancer via the inhibition of ROS generation.

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