1.Study on the dose prediction of deep learning-based VMAT after surgery for endometrial carcinoma
Yu HE ; Chune DENG ; Runhong LIU
China Medical Equipment 2024;21(3):29-33,43
Objective:To explore the predictive value of deep learning based on three dimensional deep residual network(3D Res-Unet)model for the dose accuracy of postoperative volume modulated arc therapy(VMAT)plan of endometrial carcinoma.Methods:A retrospective collection of 154 VMAT radiotherapy plans for endometrial carcinoma from The First People's Hospital of Neijiang was conducted.The data set was divided into one training set with 108 cases,one validation set with 15 cases and one test set with 31 cases as the ratio of 7:1:2 through randomly sampling.The approved dose of clinical application was used as"gold standard"to compare the difference between predictive radiotherapy dose of 3D Res-UNet and clinically radiotherapy dose.Results:There were statistical differences in the conformity index(CI)of target area and average dose(Dmean)between deep learning and clinical gold standard(t=-3.115,-0.124,P<0.05),and the difference of bladder V40 of organ at risk(OAR)between them was significant(t=0.510,P<0.05),and the difference of rectum V50 between them was significant(t=-2.121,P<0.05).The predictive dose of the left femoral head V30 was significantly lower than that of clinical dose(t=0.415,P<0.05).The predictive dose of the right femoral head V30 was significantly lower than that of clinical dose(t=-3.102,P<0.05).The predictive dose of pelvic Dmean was significantly higher than that of clinical dose(t=1.224,P<0.05).The predictive dose of small intestine V40 was significantly higher than that of clinical dose(t=0.461,P<0.05).There were no statistically significant difference in other indicators(P>0.05).The difference plot of dose showed that there was few difference between predictive results and clinical results,and the dose volume histogram of prediction basically coincided with that of clinical application.Conclusion:The 3DRes-UNet model can effectively predict the three-dimensionally spatial dose of VMAT plan after surgery for endometrial carcinoma,which can guide clinical radiotherapy work.
2.Discovery of novel covalent selective estrogen receptor degraders against endocrine-resistant breast cancer.
Yubo WANG ; Jian MIN ; Xiangping DENG ; Tian FENG ; Hebing HU ; Xinyi GUO ; Yan CHENG ; Baohua XIE ; Yu YANG ; Chun-Chi CHEN ; Rey-Ting GUO ; Chune DONG ; Hai-Bing ZHOU
Acta Pharmaceutica Sinica B 2023;13(12):4963-4982
Endocrine-resistance remains a major challenge in estrogen receptor α positive (ERα+) breast cancer (BC) treatment and constitutively active somatic mutations in ERα are a common mechanism. There is an urgent need to develop novel drugs with new mode of mechanism to fight endocrine-resistance. Given aberrant ERα activity, we herein report the identification of novel covalent selective estrogen receptor degraders (cSERDs) possessing the advantages of both covalent and degradation strategies. A highly potent cSERD 29c was identified with superior anti-proliferative activity than fulvestrant against a panel of ERα+ breast cancer cell lines including mutant ERα. Crystal structure of ERα‒ 29c complex alongside intact mass spectrometry revealed that 29c disrupted ERα protein homeostasis through covalent targeting C530 and strong hydrophobic interaction collied on H11, thus enforcing a unique antagonist conformation and driving the ERα degradation. These significant effects of the cSERD on ERα homeostasis, unlike typical ERα degraders that occur directly via long side chains perturbing the morphology of H12, demonstrating a distinct mechanism of action (MoA). In vivo, 29c showed potent antitumor activity in MCF-7 tumor xenograft models and low toxicity. This proof-of-principle study verifies that novel cSERDs offering new opportunities for the development of innovative therapies for endocrine-resistant BC.