Chinese Journal of Cancer Research最新文献

{"title":"Current trends and future directions of artificial intelligence in lung cancer diagnosis.","authors":"Weilong Hu, Gang Wang, Li Ren, Jie Hu, Xiaopeng Wu, Weihua Zhuang, Yongchao Yao, Chengdi Wang, Feng Ye, Wenjun Mao, Wenchuang Hu","doi":"10.21147/j.issn.1000-9604.2026.02.03","DOIUrl":"10.21147/j.issn.1000-9604.2026.02.03","url":null,"abstract":"<p><p>Lung cancer is the most lethal malignancy worldwide, largely due to its late detection after its progression to advanced stages. Over the last decade, artificial intelligence (AI) applications have shown significant potential in transforming lung cancer diagnostics by improving the speed, accuracy, and personalization of early detection strategies. This review provides a comprehensive overview of current AI application landscape in early lung cancer diagnosis, encompassing medical imaging, histopathology, liquid biopsy, natural language processing of electronic health records, and genomic profiling. We explain how machine learning, deep learning, and transformer-based models are employed in lung cancer diagnosis, and summarize recent cutting-edge advances, including multimodal AI platforms and Food and Drug Administration (FDA)-approved computer-aided diagnosis/detection (CAD) systems. Furthermore, we evaluate the challenges that impede clinical translation, including data heterogeneity, interpretability, and privacy, and present prospective directions such as federated learning and multi-omics integration. Through a comprehensive analysis of the dynamic evolution of AI applications in oncology, we aim to inform researchers, clinicians, and policymakers about its diagnostic potential and translational relevance in clinical practice.</p>","PeriodicalId":9882,"journal":{"name":"Chinese Journal of Cancer Research","volume":"38 2","pages":"148-176"},"PeriodicalIF":6.7,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13171413/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147962447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preface to Special Issue: AI-driven comprehensive cancer therapy: Strategy construction, basic and translational research.","authors":"Lei Zhu","doi":"10.21147/j.issn.1000-9604.2026.02.01","DOIUrl":"https://doi.org/10.21147/j.issn.1000-9604.2026.02.01","url":null,"abstract":"","PeriodicalId":9882,"journal":{"name":"Chinese Journal of Cancer Research","volume":"38 2","pages":"119-120"},"PeriodicalIF":6.3,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13171416/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147962461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CARE framework: Rethinking randomized controlled trials for medical artificial intelligence.","authors":"Sicheng Yang, Qiuxia Yang, Yize Mao, Lei Zhu","doi":"10.21147/j.issn.1000-9604.2026.02.05","DOIUrl":"10.21147/j.issn.1000-9604.2026.02.05","url":null,"abstract":"","PeriodicalId":9882,"journal":{"name":"Chinese Journal of Cancer Research","volume":"38 2","pages":"197-199"},"PeriodicalIF":6.7,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13171410/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147962431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of an interpretable machine learning model for lymphovascular space invasion prediction in patients with endometrioid endometrial carcinoma: A prospective study.","authors":"Meixuan Wu, Ling Zhou, Xiao Yang, Sihui Yang, Bowen Sun, Lirong Zhai, Chengcheng Li, Huaijun Zhou, Yuan Cheng, Jianliu Wang","doi":"10.21147/j.issn.1000-9604.2026.02.10","DOIUrl":"10.21147/j.issn.1000-9604.2026.02.10","url":null,"abstract":"<p><strong>Objective: </strong>Lymphovascular space invasion (LVSI) is a high-risk factor for lymph node metastasis, relapse, and poor prognosis in patients with endometrioid endometrial carcinoma (EEC). However, the diagnosis of LVSI still relies on traditional pathological methods. Moreover, the high-risk factors and mechanism for LVSI remain unclear. Thus, this study developed an interpretable machine learning (ML) model to accurately predict LVSI status in patients with EEC.</p><p><strong>Methods: </strong>The study collected data from 832 patients with EEC at Peking University People's Hospital. Patients were randomly divided into training (n=582) and internal validation (n=250) cohorts. A prospective external validation cohort included 129 patients with EEC from Nanjing Drum Tower Hospital. Using 21 parameters, 6 ML strategies were used to build prediction models. The global and local interpretation of feature significance was performed using the SHapley Additive exPlanations (SHAP) approach. Data from NanoString nCounter evaluation was subjected to pathway enrichment and Spearman correlation analysis to investigate the mechanistic basis of LVSI.</p><p><strong>Results: </strong>Among the six ML models, the XGBoost model had the best performance. The XGBoost model correctly predicted the risk of LVSI in the training set [area under the curve (AUC): 0.982, 95% confidence interval (95% CI): 0.972-0.991], the internal validation set (AUC: 0.818, 95% CI: 0.776-0.860), and the external test set (AUC: 0.748, 95% CI: 0.618-0.879). The calibration curve indicated that the XGBoost model exhibited favorable consistency between the predicted and actual risks. SHAP analysis identified age, carbohydrate antigen 125 (CA125), low-density lipoprotein (LDL), and neutrophil as the top four variables contributing to XGBoost model predictions. Analysis of NanoString data indicated that LVSI may be closely associated with the PI3K-Akt signaling pathway.</p><p><strong>Conclusions: </strong>We developed an interpretable ML model for preoperative LVSI risk prediction in patients with EEC. This model may aid clinicians by informing individualized clinical decision-making.</p>","PeriodicalId":9882,"journal":{"name":"Chinese Journal of Cancer Research","volume":"38 2","pages":"252-267"},"PeriodicalIF":6.7,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13171419/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147962495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating AI-driven single-cell analysis to decode epithelial heterogeneity: A prognostic signature and translational immunotherapy strategy targeting <i>KRT18</i>-induced PD-L1 in lung adenocarcinoma.","authors":"Han Zhang, Jun Zhou, Jiwei Liu, Pengpeng Zhang, Xin Li, Yue Yu, Wenjun Mao, Daqiang Sun","doi":"10.21147/j.issn.1000-9604.2026.02.04","DOIUrl":"10.21147/j.issn.1000-9604.2026.02.04","url":null,"abstract":"<p><strong>Objective: </strong>The heterogeneity of epithelial cells and their interaction with the immune microenvironment play crucial roles in tumor progression, but the underlying mechanisms remain unclear.</p><p><strong>Methods: </strong>We analyzed single-cell transcriptomic data from normal and tumor tissues to characterize epithelial cells and their microenvironment. Key genes were identified and used, via survival analysis and multiple machine learning methods, to construct a prognostic model termed the Epithelial Signature (EpiSig). We further validated, through a series of experiments, the critical immunological roles of the key genes incorporated into the EpiSig model.</p><p><strong>Results: </strong>Tumor tissues showed a marked increase in epithelial cells, a reduction in natural killer (NK)/T cells, and cell co-occurrence patterns distinct from normal tissues. We identified differentially expressed genes in tumor epithelial cells and integrated multiple machine-learning algorithms to construct the EpiSig model. This model effectively stratified patient prognosis, with the high-EpiSig group exhibiting significantly worse survival; receiver operator characteristic curve (ROC) and principal component analysis (PCA) analyses further supported its accuracy and robustness. Immune analyses indicated lower immune cell infiltration, decreased human leukocyte antigen (HLA) expression, and elevated programmed cell death ligand 1/programmed cell death protein 1 (PD-L1/PD-1) in the high-EpiSig group, reflecting a more pronounced immunosuppressive microenvironment. The core gene <i>KRT18</i> correlated strongly with EpiSig scores (R=0.65) and promoted lung adenocarcinoma (LUAD) cell proliferation in functional assays. Further, low <i>KRT18</i> tumors showed higher CD4+/CD8+ T cell and CD20+ B cell infiltration; in a mouse LLC subcutaneous tumor model, both <i>Krt18</i> knockdown and PD-1 blockade suppressed tumor growth, with greater efficacy in combination. Mechanistically, <i>KRT18</i> activated NF-kB/p65 to upregulate PD-L1, promoting immune exclusion and impairing T-cell effector function.</p><p><strong>Conclusions: </strong>This study highlights the close relationship between epithelial cell heterogeneity and immune microenvironment alterations in tumors, and presents the EpiSig as a robust tool for prognostic prediction. <i>KRT18</i> may serve as a promising therapeutic target in LUAD.</p>","PeriodicalId":9882,"journal":{"name":"Chinese Journal of Cancer Research","volume":"38 2","pages":"177-196"},"PeriodicalIF":6.7,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13171418/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147962454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Refined immune-based molecular subtypes of gastric cancer: Integrating mismatch repair status and tumor microenvironment for enhanced immunotherapy prediction.","authors":"Ke Shen, Yanqiang Zhang, Zhiqin Wang, Ruixing Xu, Jintao He, Mengya Zhou, Yiming Lu, Danni Zhu, Yizhou Wei, Weiwei Zhu, Qing Yang, Zhiyuan Xu, Can Hu, Siwei Pan, Xiangdong Cheng","doi":"10.21147/j.issn.1000-9604.2026.02.09","DOIUrl":"10.21147/j.issn.1000-9604.2026.02.09","url":null,"abstract":"<p><strong>Objective: </strong>Gastric cancer (GC) is heterogeneous, and current mismatch repair (MMR)-based classifications incompletely predict response to immune checkpoint inhibitors (ICIs).</p><p><strong>Methods: </strong>RNA sequencing (RNA-seq) and immune infiltration profiles from 189 resected GC were used to derive four refined immune-MMR subtypes (R1-R4) by integrating MMR status, survival, and tumor microenvironment (TME) features. Multi-omics profiling and pathway analysis defined subtype biology. External transcriptomic cohorts and an ICI-treated cohort were classified with Nearest Template Prediction (NTP). Immune response-associated genes were identified from responder <i>vs.</i> non-responder comparisons within the ICI-sensitive subtype and validated by multiplex immunohistochemistry (mIHC).</p><p><strong>Results: </strong>R1 showed the best prognosis and highest immunotherapy response with objective response rate (ORR) 54.5%, while R4 had the worst prognosis. R2 represented an immune-unresponsive deficient mismatch repair (dMMR) subset, and R3 captured an immune-active proficient mismatch repair (pMMR) subgroup with moderate therapy sensitivity. Multi-omics integration revealed subtype-specific pathways (e.g., ECM remodeling in R1, metabolic reprogramming in R2). Reclassification of pMMR tumors based on transcriptional similarity to R1 identified a New R3 subset with enhanced immune features and higher ICI response. Eight immune response-associated genes (e.g., <i>CXCL10</i>, <i>CXCL11</i>, <i>ELN</i>, <i>GAD1</i>, <i>IL32</i>, <i>MT1E</i>, <i>OR2I1P</i>, <i>SLC3A1</i>) were identified and validated by mIHC for predictive relevance.</p><p><strong>Conclusions: </strong>This immune-based molecular framework refines risk stratification beyond conventional MMR categories, identifies ICI-sensitive subsets among both dMMR and pMMR tumors, and proposes candidate biomarkers for patient selection.</p>","PeriodicalId":9882,"journal":{"name":"Chinese Journal of Cancer Research","volume":"38 2","pages":"234-251"},"PeriodicalIF":6.7,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13171420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147962584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence empowered biomaterials for cancer therapy: From rational design to clinical translation.","authors":"Chaonan Wang, Lie Zhu, Nan Lu, Hong Tang, Rong Wu, Zhewei Chen, Yixuan Huang, Pengfei Li, Zhe Zhao, Renjun Gu","doi":"10.21147/j.issn.1000-9604.2026.02.02","DOIUrl":"10.21147/j.issn.1000-9604.2026.02.02","url":null,"abstract":"<p><p>Tumor biomaterials show great potential for targeted cancer therapy, yet their development and clinical translation have long been hampered by inefficient empirical trial-and-error models. These traditional methods cannot fully characterize the nonlinear relationships between a material's physicochemical properties and its complex biological effects, nor can they resolve tumor heterogeneity-the primary cause of inconsistent clinical outcomes. This review systematically explores the application of artificial intelligence (AI) across the entire development pipeline of tumor biomaterials, from early rational material design to clinical treatment optimization. We show that AI addresses key bottlenecks in the field in four core ways: it speeds up novel material discovery via generative algorithms, accurately predicts the <i>in vivo</i> transport and uptake of materials, enables noninvasive and precise patient stratification, and optimizes synergistic combination treatment regimens. These advances form a data-driven closed-loop framework that connects preclinical research and clinical translation, overcoming the core limitations of traditional development models. We also outline key unresolved challenges, including data standardization, model interpretability, and regulatory compliance, and highlight AI's growing role as a core driver of precision oncology and translational medicine.</p>","PeriodicalId":9882,"journal":{"name":"Chinese Journal of Cancer Research","volume":"38 2","pages":"121-147"},"PeriodicalIF":6.7,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13171417/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147962405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-domain few-shot learning: A new perspective on overcoming bottlenecks in clinical artificial intelligence tumor diagnosis.","authors":"Ziyao Chen, Chenglong Fu, Chuang Wang, Cheng Chen, Xiang Pan, Jian Yao, Pengjiang Qian","doi":"10.21147/j.issn.1000-9604.2026.02.07","DOIUrl":"10.21147/j.issn.1000-9604.2026.02.07","url":null,"abstract":"","PeriodicalId":9882,"journal":{"name":"Chinese Journal of Cancer Research","volume":"38 2","pages":"204-208"},"PeriodicalIF":6.7,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13171414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147962388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel germline <i>JAK2 L808W</i> and <i>E305Vfs*4</i> mutations in familial essential thrombocythemia: Evidence from two pedigrees.","authors":"Jianxin Liu, Wenqing Hu, Chunjian Wang, Yayun Zhang, Min Ouyang, Hanyun Ren, Meixiang Zhang","doi":"10.21147/j.issn.1000-9604.2026.02.12","DOIUrl":"10.21147/j.issn.1000-9604.2026.02.12","url":null,"abstract":"","PeriodicalId":9882,"journal":{"name":"Chinese Journal of Cancer Research","volume":"38 2","pages":"285-288"},"PeriodicalIF":6.7,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13171421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147962493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a multiparametric magnetic resonance imaging-based nomogram for predicting feasibility of sphincter-preserving surgery in mid-low rectal cancer.","authors":"Juan Wang, Tianjing Chang, Lili Tang, Qingyang Li, Zhaoya Gao, Qingkun Gao, Haopeng Hong, Zhibo Hou, Wanlan Li, Yaping Li, Ye Han, Wenyu Wu, Hongwei Wang, Wenhan Feng, Manli Na, Jianjie Wang, Mingchuan Yu, Bin Zhang, Ming Li, Yingshi Sun, Jin Gu","doi":"10.21147/j.issn.1000-9604.2026.02.11","DOIUrl":"10.21147/j.issn.1000-9604.2026.02.11","url":null,"abstract":"<p><strong>Objective: </strong>Although distance from the inferior tumor edge to the anal verge (DTAV) is a key predictor for sphincter-preserving surgery (SPS) in mid-low rectal cancer, its utility is limited in the \"decision-gray zone\" (DTAV, 3-8 cm). Therefore, this study aimed to develop and validate a multiparametric magnetic resonance imaging-based nomogram for individualized preoperative prediction of SPS feasibility.</p><p><strong>Methods: </strong>This dual-center retrospective study included 335 patients with rectal adenocarcinoma (DTAV 3-8 cm). Patients were divided into training (n=263) and external validation (n=72) cohorts, and predictors were identified using multivariate logistic regression analysis. Model discrimination was assessed using area under the receiver operating curve (AUC) and calibration via the Hosmer-Lemeshow test. Subgroup analyses were performed across DTAV strata.</p><p><strong>Results: </strong>Four independent predictors were identified: larger DTAV [odds ratio (OR) =5.00, P<0.001)], larger pubococcygeal overlap distance (PCOD) (OR=1.08, P=0.001), transverse diameter of mesorectal fat (TMS) (OR=1.07, P=0.017), and subcutaneous adipose tissue thickness (SAT) (OR=0.94, P=0.016). The Sphincter Preservation Assessment in Rectal Cancer (SPARC) nomogram achieved an AUC of 0.928 [95% confidence interval (95% CI): 0.890-0.956] in the training cohort, outperforming DTAV alone (AUC=0.884, P=0.031) and maintaining an AUC of 0.916 (95% CI: 0.827-0.969) in external validation. Subgroup analysis showed notably improved predictions in the 5-8 cm DTAV subgroup. Decision curve analysis demonstrated a pronounced net clinical benefit across a wide range of threshold probabilities. Interobserver agreement was excellent (intraclass correlation coefficient, 0.890-0.997).</p><p><strong>Conclusions: </strong>The SPARC nomogram reliably predicted SPS feasibility by integrating tumor location with pelvic anatomy and fat distribution. This provides valuable and evidence-based preoperative guidance, especially within the DTAV 3-8 cm gray zone.</p>","PeriodicalId":9882,"journal":{"name":"Chinese Journal of Cancer Research","volume":"38 2","pages":"268-284"},"PeriodicalIF":6.7,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13171412/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147962526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}