Page 159 - 南京医科大学自然版
P. 159
第45卷第10期 阙煜轩,周晓颖. 人工智能在消化道早癌诊疗中的应用进展[J].
2025年10月 南京医科大学学报(自然科学版),2025,45(10):1521-1527,1536 ·1527 ·
[41]TIAN F,LIU D,WEI N,et al. Prediction of tumor origin 内镜杂志,2024,41(4):253-262
in cancers of unknown primary origin with cytology⁃based Big Data Collaboration Group,Digestive Endoscopology
deep learning[J]. Nat Med,2024,30(5):1309-1319 Branch of Chinese Medical Association. Expert consensus
[42]JEE J,FONG C,PICHOTTA K,et al. Automated real ⁃ on the clinical application of colonoscopy artificial intelli⁃
world data integration improves cancer outcome predic⁃ gence system(2023,Wuhan)[J]. Chin J Dig Endosc,
tion[J]. Nature,2024,636(8043):728-736 2024,41(4):253-262
[43]WANG X,ZHAO J,MAROSTICA E,et al. A pathology [53] HAMILTON A. The future of artificial intelligence in
foundation model for cancer diagnosis and prognosis pre⁃ surgery[J]. Cureus,2024,16(7):e63699
diction[J]. Nature,2024,634(8035):970-978 [54]KINOSHITA T,KOMATSU M. Artificial intelligence in
[44]NGUYEN H T,KHOA HUYNH L A,NGUYEN T V,et surgery and its potential for gastric cancer[J]. J Gastric
al. Multimodal analysis of ctDNA methylation and frag⁃ Cancer,2023,23(3):400-409
mentomic profiles enhances detection of nonmetastatic [55]GUPTA A,SINGLA T,CHENNATT J J,et al. Artificial
colorectal cancer[J]. Future Oncol,2022,18(35):3895- intelligence:a new tool in surgeon’s hand[J]. J Educ
3912 Health Promot,2022,11:93
[45]MAEDA Y,DITONNO I,PUGA⁃TEJADA M,et al. Artifi⁃ [56]NAZARIAN S,GKOUZIONIS I,KAWKA M,et al. Real⁃
cial intelligence⁃enabled advanced endoscopic imaging to time tracking and classification of tumor and nontumor tis⁃
assess deep healing in inflammatory bowel disease[J]. sue in upper gastrointestinal cancers using diffuse reflec⁃
eGastroenterology,2024,2(3):e100090 tance spectroscopy for resection margin assessment[J].
[46]WANG J,ZENG Z,LI Z,et al. The clinical application of JAMA Surg,2022,157(11):e223899
artificial intelligence in cancer precision treatment[J]. J [57]KNUDSEN J E,GHAFFAR U,MA R,et al. Clinical appli⁃
Transl Med,2025,23(1):120 cations of artificial intelligence in robotic surgery[J]. J
[47]YUAN X L,LIU W,LIN Y X,et al. Effect of an artificial Robot Surg,2024,18(1):102
intelligence ⁃ assisted system on endoscopic diagnosis of [58]中国科学院计算技术研究所. 一种基于多模态融合深
superficial oesophageal squamous cell carcinoma and pre⁃ 度 学 习 的 围 手 术 期 并 发 症 风 险 预 测 方 法 :
cancerous lesions:a multicentre,tandem,double⁃blind, CN202410548213.1[P]. 2024⁃08⁃09
randomised controlled trial[J]. Lancet Gastroenterol Hep⁃ Institute of Computing Technology,Chinese Academy of
atol,2024,9(1):34-44 Sciences. A perioperative complication risk prediction
[48]CALVARESE M,CORBETTA E,CONTRERAS J,et al. method based on multimodal fusion deep learning:
Endomicroscopic AI⁃driven morphochemical imaging and CN202410548213.1[P]. 2024⁃08⁃09
fs⁃laser ablation for selective tumor identification and se⁃ [59]FRITZ B A,KING C R,ABDELHACK M,et al. Effect of
lective tissue removal[J]. Sci Adv,2024,10(50):ea⁃ machine learning models on clinician prediction of postop⁃
do9721 erative complications:the perioperative ORACLE ran⁃
[49]ICHIMASA K,KUDO S E,MORI Y,et al. Correction:Ar⁃ domised clinical trial[J]. Br J Anaesth,2024,133(5):
tificial intelligence may help in predicting the need for ad⁃ 1042-1050
ditional surgery after endoscopic resection of T1 colorec⁃ [60]FOUNTZILAS E,PEARCE T,BAYSAl M A,et al. Con⁃
tal cancer[J]. Endoscopy,2018,50(3):C2. doi:10.1055/ vergence of evolving artificial intelligence and machine
s⁃0044-100290 learning techniques in precision oncology[J]. NPJ Digit
[50]WU L,SHANG R,SHARMA P,et al. Effect of a deep Med,2025,8(1):75
learning ⁃ based system on the miss rate of gastric neo⁃ [61]WANG J,ZENG J,LI H,et al. A deep learning radiomics
plasms during upper gastrointestinal endoscopy:a single⁃ analysis for survival prediction in esophageal cancer[J]. J
centre,tandem,randomised controlled trial[J]. Lancet Healthc Eng,2022,2022:4034404
Gastroenterol Hepatol,2021,6(9):700-708 [62]ZHANG W,FANG M,DONG D,et al. Development and
[51]YUAN X L,ZENG X H,LIU W,et al. Artificial intelli⁃ validation of a CT⁃based radiomic nomogram for preopera⁃
gence for detecting and delineating the extent of superfi⁃ tive prediction of early recurrence in advanced gastric
cial esophageal squamous cell carcinoma and precancer⁃ cancer[J]. Radiother Oncol,2020,145:13-20
ous lesions under narrow⁃band imaging(with video)[J]. [63]BAHRAMBANAN F,ALIZAMIR M,MORADVEISI K,et
Gastrointest Endosc,2023,97(4):664-672 al. The development of an efficient artificial intelligence⁃
[52]中华医学会消化内镜学分会大数据协作组.肠镜人工 based classification approach for colorectal cancer re⁃
智能系统临床应用专家共识(2023,武汉)[J].中华消化 (下转第1536页)

