Objective:To validate the feasibility of a deep learning-based clinical target volume (CTV) auto-segmentation algorithm for cervical cancer in clinical settings.Methods:CT data sets from 535 cervical cancer patients were collected. CTVs were delineated according to RTOG and JCOG guidelines, reviewed by experts, and then used as reference contours for training (definitive 177, post-operative 302) and test (definitive 23, post-operative 33). Four definitive and 6 post-operative cases were randomly selected from the testing cohort to be manually delineated by junior, intermediate, senior doctors, respectively. Dice coefficient (DSC), mean surface distance (MSD) and Hausdorff distance (HD) were used for test and comparison between auto-segmentation and RO delineation. Meantime, auto-segmentation time and manual delineation time were recorded.Results:Auto-segmentation models of dCTV 1, dCTV 2 and pCTV 1 were trained with VB-Net and showed good agreement with reference contours in the testing cohorts (DSC, 0.88, 0.70, 0.86 mm; MSD, 1.32, 2.42, 1.15 mm; HD, 21.6, 22.4, 20.8 mm). For dCTV 1, the difference between auto-segmentation and all three groups of doctors was not significant ( P>0.05). For dCTV 2 and pCTV 1, auto-segmentation was better than the junior and intermediate doctors (both P<0.05). Auto-segmentation time consumption was considerably shorter than that of manual delineation. Conclusions:Deep learning-based CTV auto-segmentation algorithm for cervical cancer achieves comparable accuracy to manual delineation of senior doctors. Clinical application of the algorithm can contribute to shortening doctors′ manual delineation time and improving clinical efficiency. Furthermore, it may serve as a guide for junior doctors to improve the consistency and accuracy of cervical cancer CTV delineation in clinical practice.

Objective To investigate the effect of insulin-like growth factor 2 mRNA binding protein 2 (IGF2BP2) on the proliferation, migration and tumor immune microenvironment of colorectal cancer cells and its possible molecular mechanism. Methods The Cancer Genome Atlas (TCGA) database was used to analyze the expression levels of IGF2BP2 and MYC in colorectal cancer and adjacent tissues. The expression of IGF2BP2 in HCT-116 and SW480 human colorectal cancer cells was silenced by RNA interference (RNAi), and the silencing effect was detected by quantitative real-time PCR. After knocking down IGF2BP2, colony formation assay, CCK-8 assay and 5-ethynyl-2'-deoxyuridine (EdU) assay were employed to detect cell colony formation and proliferation ability. Transwell^TM assay was used to detect cell migration ability. Quantitative real-time PCR was used to detect the mRNA expression of IGF2BP2, MYC, tumor necrosis factor-α (TNF-α), transforming growth factor-β (TGF-β) and interleukin-10 (IL-10). The protein expression of IGF2BP2 and MYC was detected by western blot. The binding ability of IGF2BP2 and MYC in HCT-116 cells was detected by quantitative real-time PCR after RNA immunoprecipitation. Results The results of TCGA database showed that the expression of IGF2BP2 and MYC in colorectal cancer tissues was significantly higher than that in adjacent tissues, and the survival time of colorectal cancer patients with high expression of IGF2BP2 was shorter. After silencing IGF2BP2, the viability, proliferation and migration of HCT-116 and SW480 cells were decreased. The mRNA expression of MYC, TGF-β and IL-10 in IGF2BP2 knockdown group was significantly decreased, while the expression of TNF-α mRNA was increased. The expression of MYC protein and the stability of MYC mRNA were significantly decreased. RIP-qPCR results showed that IGF2BP2 could bind to MYC mRNA. Conclusion Knockdown of IGF2BP2 inhibits colorectal cancer cell proliferation, migration and promotes tumor immunity by down-regulating MYC expression.
Humans ; Cell Line, Tumor ; Cell Movement/genetics* ; Cell Proliferation/genetics* ; Colorectal Neoplasms/metabolism* ; Gene Expression Regulation, Neoplastic ; Interleukin-10/metabolism* ; RNA, Messenger ; RNA-Binding Proteins/metabolism* ; Transforming Growth Factor beta/genetics* ; Tumor Microenvironment/immunology* ; Tumor Necrosis Factor-alpha/metabolism* ; Proto-Oncogene Proteins c-myc/metabolism*