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08 August 2026, Volume 53 Issue 8 Previous Issue   

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Original Article

irTD predictive model and a real cohort study on the survival benefits in advanced tumor immunotherapy Li Xueqin, Gong Quan, Zhang Lijuan, Chen Xi, Li Shijuan, Zhou Chunyan, Li Jun, Zhuang Li 2026, 53 (8):  452-463.  doi: 10.3760/cma.j.cn371439-20250719-00074 Abstract ( 23 )   HTML ( 3 )   PDF (1296KB) ( 0 )   Save Objective To investigate the influencing factors of immune-related thyroid dysfunction （irTD） and its impact on survival prognosis in patients with advanced malignant tumors， and to analyze the regulatory role of metabolic factors. Methods A total of 70 patients with advanced tumors who received immunotherapy at the Third Affiliated Hospital of Kunming Medical University from January 1， 2018 to January 31， 2024 were selected as the study subjects. Based on the occurrence of thyroid dysfunction after immune checkpoint inhibitors （ICIs） treatment， patients were divided into the irTD group （n=53） and the non-irTD group （n=17）. The relevant indicators of the patients in the irTD group before and after immunotherapy were compared. Receiver operator characteristic （ROC） curves were plotted to evaluate the predictive efficacy of each indicator for irTD in advanced tumor patients after ICIs treatment. Multivariate logistic regression was used to identify the risk factors for irTD in tumor patients after ICIs treatment. Kaplan-Meier survival curves were plotted for survival analysis， and the log-rank test was performed. A stratified Cox proportional hazards regression model was used to evaluate the influencing factors of prognosis in patients with advanced tumors. Results In tumor patients with irTD， serum levels before and after immunotherapy were as follows： thyroid-stimulating hormone （TSH） was 3.24 （2.16， 4.01） and 12.20 （0.03， 73.55） μIU/ml； thyroglobulin antibody （TGAb） was 19.87 （12.82， 216.00） and 29.47 （15.92， 436.80） IU/ml； thyroid peroxidase antibody （TPOAb） was 4.04 （2.00， 130.43） and 42.28 （2.00， 400.00） IU/ml； and thyroglobulin （TG） was 5.67 （1.48， 17.81） and 22.80 （2.55， 52.15） ng/ml， with statistically significant differences （Z=4.36， P<0.001； Z=4.81， P<0.001； Z=4.64， P<0.001； Z=4.30， P<0.001）. ROC curve analysis showed that baseline TSH， TGAb， TPOAb， and suppressor T （Ts） cell levels had areas under the curve （AUCs） for predicting irTD after immunotherapy of 0.663， 0.704， 0.737， and 0.691， respectively， but pairwise comparisons revealed no significant differences in predictive efficacy （all P>0.05）. Multivariate analysis identified TSH≥1.87 μIU/ml （OR=4.12， 95%CI： 1.10-15.46， P=0.036） and baseline antibody positivity （OR=10.68， 95%CI： 1.21-94.43， P=0.033） as independent risk factors for irTD after immunotherapy in tumor patients. Survival analysis showed that the median overall survival （mOS） was 29 months in the irTD group and 22 months in the non-irTD group， with 1-year overall survival （OS） rates of 88.5% and 76.0%， respectively， with a statistically significant difference （χ2=4.85， P=0.028）. The subgroup analysis showed that in patients aged ≥65 years， receiving first-line therapy， with comorbid hyperlipidemia， without hyperuricemia， without diabetes， without combined surgery， with combined targeted therapy， with C-reactive protein-albumin-lymphocyte score ≤3， body mass index （BMI）<18.5 kg/m2 or 18.5 kg/m2≤BMI<24 kg/m2， the mOS in the irTD group was significantly longer than that in the non-irTD group （all P<0.05）. The mOS for patients with lung adenocarcinoma， lung squamous cell carcinoma， small cell lung cancer， and other histological subtypes in the irTD group was 43， 29， 18， and 15 months， respectively， with a statistically significant difference （χ2=8.67， P=0.034）. However， pairwise comparisons showed that there were no statistically significant differences after Bonferroni correction （all adjusted P>0.008 3）. Multivariate analysis revealed that among all patients receiving ICI treatment， sex （HR=0.39， 95%CI： 0.18-0.84， P=0.016）， treatment line （HR=0.33， 95%CI： 0.13-0.85， P=0.021）， occurrence of irTD （HR=0.43， 95%CI： 0.19-0.95， P=0.037）， and surgery （HR=4.38， 95%CI： 1.26-10.55， P=0.017） were all independent influencing factors for OS. Among patients who developed irTD， further subgroup analysis stratified by TSH level showed that all Eastern Cooperative Oncology Group performance status score （HR=0.19， 95%CI： 0.05-0.83， P=0.027）， BMI grade （HR=0.54， 95%CI： 0.29-0.98， P=0.043）， and TSH>100 μIU/ml （HR=0.04， 95%CI： 0.00-0.60， P=0.021） were independent influencing factors for OS in this population. Conclusions Baseline TSH≥1.87 μIU/ml and elevated thyroid autoantibodies are independent predictors of irTD in patients with advanced tumors. The occurrence of irTD is associated with prolonged mOS， and this survival benefit persists in patients with comorbid hyperlipidemia， as well as in those without hyperuricemia or diabetes， suggesting that metabolic status may influence the prognostic value of irTD. Among patients with irTD， TSH>100 μIU/ml is an independent protective factor for OS. Figures and Tables | References | Related Articles | Metrics

Construction of a nomogram prediction model for evaluating the therapeutic efficacy of hyperbaric oxygen chamber therapy in managing acute radiation dermatitis among breast cancer patients Niu Ping, Tian Long, Cui Ling 2026, 53 (8):  464-469.  doi: 10.3760/cma.j.cn371439-20251015-00075 Abstract ( 16 )   HTML ( 1 )   PDF (1134KB) ( 0 )   Save Objective To analyze the factors affecting the efficacy of hyperbaric oxygen chamber in treating acute radiation dermatitis （ARD） caused by breast cancer radiotherapy， and to construct a nomogram prediction model. Methods A total of 720 breast cancer patients who underwent modified radical mastectomy and radiotherapy in the Department of Radiation Oncology of the First Affiliated Hospital of Hebei North University from December 2021 to December 2024 were selected as research subjects. Using the random drawing method， patients were divided into the model group （n=504） and the validation group （n=216） in a ratio of 7∶3. On the basis of conventional ARD prevention measures， the patients received 15 sessions of hyperbaric oxygen chamber therapy. At week 7， multivariate logistic regression was used to analyze the influencing factors for patients with ARD grade 1 in the model group. A efficacy nomogram prediction model was constructed. The model's consistency was assessed using the C-index and calibration curves. Receiver operator characteristic （ROC） curve was plotted for patients with grade 1 ARD in both the model group and the validation group， and the area under the curve （AUC） was compared between groups using the DeLong test. Results There were statistically significant differences in age （t=-9.05， P<0.001）， history of chemotherapy （χ2=4.47， P=0.035）， hypertension （χ2=5.40， P=0.020）， diabetes （χ2=5.34， P=0.021）， microcirculation disorder （χ2=4.14， P=0.042）， and arteriosclerosis （χ2=4.12， P=0.042） between patients with grade 1 and grade 2 or above ARD at the 7th week of radiotherapy. Multivariate analysis revealed that age （OR=1.56， 95%CI： 1.09-1.93， P=0.031）， history of chemotherapy （OR=2.30， 95%CI： 1.66-4.78， P<0.001）， hypertension （OR=1.79， 95%CI： 1.24-2.78， P=0.026）， diabetes （OR=1.88， 95%CI： 1.39-3.25， P=0.019）， microcirculation disorder （OR=2.15， 95%CI： 1.58-4.46， P=0.004）， and atherosclerosis （OR=2.01， 95%CI： 1.49-4.27， P=0.010） were all independent influencing factors for grade 1 ARD in breast cancer patients at the 7th week of radiotherapy after radical mastectomy. The C-index of the nomogram prediction model constructed based on the above factors was 0.862 （95%CI： 0.824-0.895）， and the calibration curve showed good consistency （χ2=5.89， P=0.177）. The AUCs of the nomogram model for predicting grade 1 ARD in breast cancer patients at the 7th week of radiotherapy after radical mastectomy in the model group and validation group were 0.852 and 0.839， respectively， with no statistically significant difference （Z=0.48， P=0.328）. Conclusions For patients aged 51 or younger， without a history of chemotherapy， without hypertension， without diabetes， without microcirculation disorders， without arteriosclerosis， and who have developed ARD after radical mastectomy for breast cancer， hyperbaric oxygen chamber therapy demonstrates a relatively favorable therapeutic outcome. A nomogram prediction model developed based on the above factors is helpful for timely and accurately screening patients suitable for this intervention， offering certain clinical applicability and reference value. Figures and Tables | References | Related Articles | Metrics

Optimal selection of dual-energy CT images in the display of gastric cancer lesions Cai Jianan, He Bosheng, Yang Jushun, Wang Xiaoyu 2026, 53 (8):  470-475.  doi: 10.3760/cma.j.cn371439-20251031-00076 Abstract ( 23 )   HTML ( 1 )   PDF (1752KB) ( 1 )   Save Objective To explore the value of the Monoenergetic+ algorithm in dual-energy CT （DECT） for optimizing the display of gastric cancer lesions. Methods A total of 48 gastric cancer patients who underwent abdominal DECT contrast-enhanced examinations at Nantong First People's Hospital， Jiangsu Province from October 2023 to December 2024 were enrolled as study subjects. Using the Syngovia post-processing workstation and the Monoenergetic+ algorithm， a total of 12 groups of arterial phase and venous phase images （40-90 keV） were obtained. The 100 kVp and 140 kVp images of the arterial phase and venous phase were fused with a coefficient of 0.5 to obtain 2 groups of 120 kVp fused images. The cross-sectional images with a layer thickness of 2 mm and a layer spacing of 2 mm were reconstructed for subjective and objective assessment， and for comparing the subjective and objective score differences of each energy level. Results In terms of subjective scoring， for the arterial phase， the image scores at 120 kVp and 40， 50， 60， 70， 80， and 90 keV were 4 （4， 4）， 5 （5， 5）， 4 （4， 5）， 3 （3， 4）， 3 （3， 3）， 2 （2， 3）， and 2 （2， 2） points， respectively. For the venous phase， the image scores of the above 7 groups were 4 （4， 5）， 5 （5， 5）， 4 （4， 5）， 3 （3， 4）， 3 （3， 3）， 2 （2， 3）， and 2 （2， 2） points， respectively， with statistically significant differences （H=259.67， P<0.001； H=258.37， P<0.001）. Further pairwise comparison results showed that， in both arterial and venous phases， the scores of 40 keV images were significantly higher than those of the concurrent 120 kVp fused images and other monoenergetic images （all P<0.05）. Regarding objective scoring， the signal-to-noise ratios （SNR） of the above 7 groups of images in the arterial phase were 8.23±3.16， 5.00±1.91， 4.74±1.65， 4.65±1.65， 4.58±1.62， 4.37±1.74， and 4.38±1.53， respectively， and the contrast-to-noise ratios （CNR） were 2.81 （1.45， 3.91）， 3.46 （2.81， 4.93）， 2.86 （2.01， 3.83）， 2.28 （1.59， 3.36）， 1.80 （1.14， 2.74）， 1.32 （0.50， 2.30）， and 0.84 （0.04， 1.48）， respectively， with statistically significant differences （F=70.99， P<0.001； H=234.76， P<0.001）. Pairwise comparison results showed that the SNR of the 120 kVp fused images in the arterial phase was significantly higher than that of each monoenergetic image （all P<0.05）； whereas the CNR of the 40 keV images in the arterial phase was significantly higher than that of other monoenergetic images and the 120 kVp fused images （all P<0.05）. The SNR of the above 7 groups of images in the venous phase were 9.27±2.81， 6.17±2.20， 5.81±2.13， 5.57±1.91， 5.47±1.74， 5.40±1.76， and 5.38±1.59， respectively， and the CNR were 3.48 （2.45， 4.61）， 4.23 （3.44， 5.59）， 3.53 （2.89， 4.59）， 2.97 （2.33， 3.92）， 2.38 （1.59， 3.39）， 1.87 （1.18， 2.65）， and 1.30 （0.70， 2.23）， respectively， with statistically significant differences （F=82.34， P<0.001； H=256.45， P<0.001）. Pairwise comparison results showed that the SNR of the 120 kVp fused images in the venous phase was significantly higher than that of each monoenergetic image （all P<0.05）， and the SNR of the 40 keV images in the venous phase was significantly higher than that of other monoenergetic images （all P<0.05）； whereas the CNR of the 40 keV monoenergetic image in the venous phase was significantly higher than that of other monoenergetic images and the 120 kVp fused images （all P<0.05）. Conclusions The 40 keV monoenergetic image obtained by the DECT Monoenergetic+ algorithm can significantly optimize the display of gastric cancer lesions and is helpful for the identification and observation of gastric cancer lesions. Figures and Tables | References | Related Articles | Metrics

Construction of a postoperative survival prediction model for elderly patients with colorectal cancer based on machine learning algorithms Shen Cailu, Wu Ruirong, Wu You, Ge Xiaosong 2026, 53 (8):  476-484.  doi: 10.3760/cma.j.cn371439-20250823-00077 Abstract ( 19 )   HTML ( 1 )   PDF (3103KB) ( 2 )   Save Objective To construct a postoperative survival prediction model suitable for elderly patients with colorectal cancer （CRC） based on machine learning algorithms， as well as to identify key factors influencing the prognosis of patients. Methods Clinical data of 3 576 elderly CRC patients who underwent surgical treatment between 2000 and 2019 were obtained from the United States Surveillance， Epidemiology， and End Results （SEER） database. The dataset was randomly divided into the training cohort （n=2 503） and the validation cohort （n=1 073） at a 7∶3 ratio. Least absolute shrinkage and selection operator （LASSO） regression was applied to identify key predictors of cancer-specific survival （CSS）， followed by the construction of LASSO-Cox and DeepSurv deep learning models. Model performance was evaluated using the concordance index （C-index）， receiver operator characteristic （ROC） curves， and Brier score. To analyze the interpretability of the DeepSurv model， the Shapley additive explanation （SHAP） method was applied. The Kaplan-Meier survival curve was plotted and the log-rank test was performed to evaluate the risk stratification ability of the model. The decision curve analysis （DCA） was used to verify the clinical practicability. Results There were no statistically significant differences in baseline characteristics between the training cohort and validation cohort （all P>0.05）. Eight key prognostic factors influencing CSS in elderly CRC patients were screened out using LASSO regression： age， T stage， N stage， histological grade， perineural invasion， positive lymph node ratio， preoperative serum carcinoembryonic antigen （CEA）， and postoperative adjuvant chemotherapy. Multivariate analysis confirmed that N stage （N1： HR=2.43， 95%CI： 1.86-3.17， P<0.001； N2： HR=3.49， 95%CI： 2.32-5.27， P<0.001）， absence of postoperative adjuvant chemotherapy （HR=1.94， 95%CI： 1.51-2.50， P<0.001） ， age≥85 years （HR=1.49， 95%CI： 1.22-1.82， P<0.001）， positive perineural invasion （HR=1.61， 95%CI： 1.26-2.04， P<0.001）， positive preoperative serum CEA （HR=1.44， 95%CI： 1.19-1.75， P<0.001）， histological grade Ⅲ-Ⅳ （HR=1.49， 95%CI： 1.20-1.85， P<0.001）， positive lymph node ratio≥0.09 （HR=1.48， 95%CI： 1.09-2.01， P=0.012）， and T3-4 stage （HR=1.47， 95%CI： 1.09-1.99， P=0.012） were independent risk factors for CSS in elderly patients with CRC. In the training cohort， the DeepSurv model yielded areas under the curve （AUCs） of 0.727 （95%CI： 0.681-0.769）， 0.753 （95%CI： 0.720-0.791）， and 0.744 （95%CI： 0.717-0.773） for predicting 1-， 2-， and 3-year CSS rate， respectively. The LASSO-Cox model yielded corresponding AUCs of 0.720 （95%CI： 0.675-0.764）， 0.748 （95%CI： 0.714-0.782）， and 0.741 （95%CI： 0.712-0.771）. In the validation cohort， the DeepSurv model achieved AUCs of 0.758 （95%CI： 0.699-0.818）， 0.786 （95%CI： 0.743-0.830）， and 0.778 （95%CI： 0.737-0.819） for predicting 1-， 2-， and 3-year CSS rate， respectively. The LASSO-Cox model demonstrated similar performance， with AUCs of 0.757 （95%CI： 0.701-0.812）， 0.783 （95%CI： 0.736-0.829）， and 0.768 （95%CI： 0.724-0.810）. The LASSO-Cox model achieved Brier scores of 0.064， 0.105， and 0.143 for predicting 1-， 2-， and 3-year CSS rate in the training cohort， and 0.058， 0.097， and 0.135 in the validation cohort. The DeepSurv model achieved Brier scores of 0.060， 0.093， and 0.127 in the training cohort， and 0.056， 0.087， and 0.120 in the validation cohort. SHAP analysis identified N stage， age， and postoperative adjuvant chemotherapy as the three most influential variables affecting the predictive performance of the DeepSurv model. Survival analysis revealed that the 3-year CSS rates for the low- and high-risk groups predicted by the LASSO-Cox model were 92.49% and 76.73% in the training cohort （χ2=132.21， P<0.001）， and 94.31% and 76.88% in the validation cohort （χ2=69.07， P<0.001）. The corresponding rates predicted by the DeepSurv model were 92.60% and 76.40% in the training cohort （χ2=142.34， P<0.001）， and 94.60% and 76.20% in the validation cohort （χ2=76.70， P<0.001）. The DCA curve showed that both models had good clinical practicability. Conclusions N stage， age， postoperative adjuvant chemotherapy， positive lymph node ratio， preoperative serum CEA， histological grade， perineural invasion， and T stage are identified as independent prognostic factors in elderly patients with CRC. The LASSO-Cox model and the DeepSurv deep learning model developed based on these variables demonstrate favorable predictive performance and robust risk stratification ability. Figures and Tables | References | Related Articles | Metrics

Review

Formation mechanism of senescent microenvironment after glioblastoma radiotherapy and research progress on targeted strategies Gao Jian, Zhu Wei 2026, 53 (8):  485-490.  doi: 10.3760/cma.j.cn371439-20251031-00078 Abstract ( 18 )   HTML ( 0 )   PDF (827KB) ( 1 )   Save Glioblastoma is the most malignant primary brain tumor， and patients often experience poor prognosis due to radiotherapy resistance and tumor recurrence following standard chemoradiotherapy. Recent studies have revealed that radiotherapy not only kills tumor cells but also induces cellular senescence in tumor cells and surrounding astrocytes， endothelial cells， etc.， thereby forming a senescent microenvironment that promotes tumor progression. Senescent cells secrete a variety of cytokines via the senescence-associated secretory phenotype， which remodels the immune and vascular microenvironments， facilitating tumor regeneration and treatment resistance. Regarding these mechanisms， the senescent cell clearance agents and senescence phenotype inhibitors have demonstrated significant tumor-suppressive effects in preclinical studies. A systematic discussion on the formation mechanisms of the senescent microenvironment and its role in radiotherapy resistance in glioblastoma， as well as the summary and outlook of therapeutic strategies targeting this microenvironment， can provide new research directions for reducing the risk of tumor recurrence. Figures and Tables | References | Related Articles | Metrics

Application progress of high-dose furmonertinib in NSCLC with drug-resistant EGFR mutation Zeng Jiao, Ding Jianghua 2026, 53 (8):  491-495.  doi: 10.3760/cma.j.cn371439-20250929-00079 Abstract ( 20 )   HTML ( 0 )   PDF (852KB) ( 0 )   Save Furmonertinib， a third-generation epidermal growth factor receptor （EGFR） tyrosine kinase inhibitor independently developed in China， has demonstrated remarkable efficacy in the treatment of EGFR-mutant non-small cell lung cancer （NSCLC）， particularly offering significant advantages for patients with T790M resistance mutations and central nervous system metastases. High-dose furmonertinib （160-240 mg/d） has been used for NSCLC with resistance-related EGFR mutations such as P-cyclin-dependent kinase α-C helix compression mutations and soft meningeal metastases. Systematically elaborating on the application progress of high-dose furmonertinib in drug-resistant NSCLC can provide evidence-based support for optimizing the treatment strategies for refractory NSCLC. References | Related Articles | Metrics

Research progress of TROP2-targeted antibody-drug conjugates for the treatment of advanced non-small cell lung cancer Qiao Dan, Nie Lei, Zhang Yili, Liu Jia, Chen Wenjuan 2026, 53 (8):  496-501.  doi: 10.3760/cma.j.cn371439-20251223-00080 Abstract ( 16 )   HTML ( 2 )   PDF (872KB) ( 0 )   Save Lung cancer remains a leading cause of global cancer mortality. Subsequent treatment options are limited for patients with non-small cell lung cancer （NSCLC） resistant to first-line immunotherapy or targeted therapy. Trophoblast cell-surface antigen 2 （TROP2）， widely expressed in NSCLC and associated with enhanced tumor invasiveness and poor prognosis， has emerged as a key target for antibody-drug conjugates （ADCs）. TROP2-ADCs have demonstrated certain efficacy in advanced NSCLC patients who have progressed after chemotherapy， immunotherapy， or targeted therapy， while combination strategies also show potential synergy. A thorough exploration of the mechanism of action of TROP2-ADC and related clinical research progress can provide references for the optimization of second-line treatment strategies and the direction of future research. References | Related Articles | Metrics

Research progress of tumor microenvironment in the pathogenesis of prostate cancer Che Yunan, Chen Xiang, Chu Xu, Lu Daofeng, Jiang Kun 2026, 53 (8):  502-507.  doi: 10.3760/cma.j.cn371439-20250929-00081 Abstract ( 16 )   HTML ( 0 )   PDF (857KB) ( 2 )   Save Prostate cancer is a highly heterogeneous disease with a complex cellular ecosystem in the tumor microenvironment. The tumor microenvironment plays a crucial role in the occurrence and development of prostate cancer. Given its significant function in supporting tumor tissues growth， related research is increasingly focusing on this field. Relevant studies have shown that cellular and functional heterogeneity in the tumor microenvironment can promote the development of more effective disease treatment strategies. In particular， the interaction and co-evolution of tumor tissues and host cells in the tumor microenvironment can lead to novel therapeutic combination models that have great potential for treating and ultimately curing tumors. References | Related Articles | Metrics