標(biāo)志性成果 |
1. 國(guó)家自然科學(xué)基金:基于MRI影像組學(xué)和深度學(xué)習(xí)的結(jié)直腸肝轉(zhuǎn)移共識(shí)分子分型及靶向治療預(yù)后預(yù)測(cè)研究(項(xiàng)目編號(hào):82171923,起止年限:2022.01-2025.12,資助額度:55萬(wàn)元,在研,主持) 2. 山西省技術(shù)創(chuàng)新中心項(xiàng)目(領(lǐng)域類):山西省腫瘤影像人工智能技術(shù)創(chuàng)新中心(起止年限:2024-01-01 至今資助額度:100萬(wàn)元,在研,主持) 3. 山西省中央引導(dǎo)地方科技發(fā)展資金項(xiàng)目:智能醫(yī)學(xué)影像分析與應(yīng)用創(chuàng)新基地(項(xiàng)目編號(hào)YDZJSX20231B012起止年限:2023.3-2026.2 資助額度:160萬(wàn)元,在研,主持) 4. Cui Y#, Zhang J#, Li Zh#, Wei K#, Ye Lei, Ren J, Wu L, Shi Zh, Meng X*, Yang X*(楊曉棠), Gao X*. A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: a multicenter cohort study. EclinicalMedicine. 2022. 46: 101348.(2021IF=17.033) 5. Liu Y#, Wang Y#, Wang Y#, Xie Y#, Cui Y, Feng S, Yao M, Qiu B, Shen W, Chen D, Du G, Chen X, Liu Z, Li Z, Yang X* (楊曉棠), Liang C*, Wu L*. Early prediction of treatment response to neoadjuvant chemotherapy based on longitudinal ultrasound images of HER2-positive breast cancer patients by Siamese multi-task network: A multicentre, retrospective cohort study. EclinicalMedicine. 2022; 52:101562. (2021 IF=17.033)(2021IF=17.033) 6. Zhang J#, Cui Y#, Wei K#, et,al.Yang X* (楊曉棠).Deep Learning Predicts Resistance to Neoadjuvant Chemotherapy for Locally Advanced Gastric Cancer: A Multicenter Study.Gastric Cancer.2022, 25(6): 1050-1059.(2021 IF=7.701) 7. 學(xué)術(shù)榮譽(yù):山西省學(xué)術(shù)技術(shù)帶頭人,山西省腫瘤影像人工智能技術(shù)創(chuàng)新中心主任,享受國(guó)務(wù)院特殊津貼 8. 科技獎(jiǎng)勵(lì):山西省科技進(jìn)步二等獎(jiǎng)2項(xiàng)、三等獎(jiǎng)1項(xiàng) 9. 學(xué)術(shù)任職:中國(guó)抗癌協(xié)會(huì)腫瘤影像專業(yè)委員會(huì)副主委、中華放射學(xué)會(huì)乳腺專業(yè)委員會(huì)委員、中國(guó)醫(yī)師協(xié)會(huì)放射醫(yī)師分會(huì)乳腺影像專業(yè)委員會(huì)副主任委員 |