ArticleCell reports. Medicine2024
Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging.
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Article in Cell reports. Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Artificial intelligence and transforming cancer care.Discover oncology · 2026Review
- Targeting signaling pathways in lymphoma: From molecular mechanisms to clinical breakthroughs.Chinese medical journal · 2026Review
- A revised staging system for natural killer/T cell lymphoma incorporating skull base invasion and nasal/non-nasal subtype distinction.Cell reports. Medicine · 2026Article
- [Analysis of clinical characteristics and delays in diagnosis of the lymphoma of sinonasal cavities].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2026Article
- Preoperative 3D morphological phenotyping of hepatocellular carcinoma via a foundation model: a non-invasive biomarker for recurrence risk stratification.eGastroenterology · 2026Article
- Application of Artificial Intelligence in Medical Education: A Systematic and Narrative Review of Pedagogical Potential and Ethical Implications.Advances in medical education and practice · 2026Review
- CNN-based detection of pediatric lymphoma on whole body [American journal of nuclear medicine and molecular imaging · 2026Article
- Non-Invasive Tumor Budding Evaluation and Correlation with Treatment Response in Bladder Cancer: A Multi-Center Cohort Study.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Deep learning-based prediction of HER2 status and trastuzumab treatment efficacy of gastric adenocarcinoma based on morphological features.Journal of translational medicine · 2025Article
- Mapping the landscape and research trend of imaging diagnosis in lymphoma: a bibliometric analysis from 1976 to 2024.Frontiers in medicine · 2025Article
- A prognostic index for advanced-stage extranodal natural killer/T-cell lymphoma: A multicenter study.Annals of hematology · 2025Article
- The Role of Artificial Intelligence and Radiomics in the Management of Lymphomas by PET/CT: The Clairvoyance in Clinic.Cancer management and research · 2025Review
- Artificial Intelligence Applications in Lymphoma Diagnosis and Management: Opportunities, Challenges, and Future Directions.Journal of multidisciplinary healthcare · 2024Review
- Research hotspots and trends of artificial intelligence in lymphoma: A bibliometric analysis from 2010 to 2024.Digital healthArticle
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Authors and funding
28 authors.
YuChen ZhangState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China.
YiShu DengState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Information Technology Center, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, P.R. China.
QiHua ZouState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China.
BingZhong JingState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Information Technology Center, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China.
PeiQiang CaiState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, P.R. China.
XiaoPeng TianState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China.
Yu YangDepartment of Lymphadenoma and Head & Neck Medical Oncology, Fujian Provincial Cancer Hospital & Institute, Fuzhou, P.R. China.
BingZong LiDepartment of Hematology, The Second Affiliated Hospital of Suzhou University, Jiangsu, P.R. China.
Fang LiuDepartment of Pathology, The First People's Hospital of Foshan, Foshan, P.R. China.
ZhiHua LiDepartment of Oncology, Sun Yat-sen Memorial Hospital, Guangzhou, Guangdong, P.R. China.
ZaiYi LiuDepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, P.R. China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, P.R. China.
ShiTing FengDepartment of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, P.R. China.
TingSheng PengDepartment of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, P.R. China.
YuJun DongDepartment of Hematology, Peking University First Hospital, Beijing 100034, P.R. China.
XinYan WangDepartment of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, P.R. China.
GuangYing RuanState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, P.R. China.
Yun HeState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, P.R. China.
ChunYan CuiState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, P.R. China.
Jiao LiState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, P.R. China.
Xiao LuoState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, P.R. China.
HuiQiang HuangState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China.
HaoHua ChenState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Information Technology Center, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China.
SongQi LiZhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, P.R. China.
Ying SunState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China.
ChuanMiao XieState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, P.R. China. Electronic address: xchuanm@sysucc.org.cn.
Liang WangDepartment of Hematology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, P.R. China. Electronic address: wangliangtrhos@126.com.
ChaoFeng LiState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Information Technology Center, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China. Electronic address: lichaofeng@sysucc.org.cn.
QingQing CaiState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China. Electronic address: caiqq@sysucc.org.cn.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate diagnosis and prognosis prediction are conducive to early intervention and improvement of medical care for natural killer/T cell lymphoma (NKTCL). Artificial intelligence (AI)-based systems are developed based on nasopharynx magnetic resonance imaging. The diagnostic systems achieve areas under the curve of 0.905-0.960 in detecting malignant nasopharyngeal lesions and distinguishing NKTCL from nasopharyngeal carcinoma in independent validation datasets. In comparison to human radiologists, the diagnostic systems show higher accuracies than resident radiologists and comparable ones to senior radiologists. The prognostic system shows promising performance in predicting survival outcomes of NKTCL and outperforms several clinical models. For patients with early-stage NKTCL, only the high-risk group benefits from early radiotherapy (hazard ratio = 0.414 vs. late radiotherapy; 95% confidence interval, 0.190-0.900, p = 0.022), while progression-free survival does not differ in the low-risk group. In conclusion, AI-based systems show potential in assisting accurate diagnosis and prognosis prediction and may contribute to therapeutic optimization for NKTCL.
Indexed as
Artificial IntelligenceMagnetic Resonance ImagingAdultAgedFemaleHumansLymphoma, Extranodal NK-T-CellMaleMiddle AgedPrognosisartificial intelligencediagnosisnatural killer/T cell lymphomapredictive modelprognosis
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