This study primarily aimed to identify prognostic factors for patients with recurrent/metastatic tongue squamous cell carcinoma (R/M TSCC) undergoing re-resection and to preliminarily explore the value of baseline metabolic parameters in predicting pathological response after neoadjuvant therapy (NAT). In the primary analyses, we analyzed 114 patients with R/M TSCC who underwent direct re-resection or re-resection after NAT between 2017 and 2024. Clinical, pathological, and PET/CT parameters were collected. Lasso-Cox regression was performed to identify factors influencing disease-free survival (DFS) and overall survival (OS), with internal validation via bootstrapping with 1000 resamples. Sensitivity analysis was performed to test the robustness of the main findings by restricting the analysis to the 100 patients who underwent elective neck dissection (a subgroup of the 114 patients). Patients undergoing re-resection after NAT were grouped by pathological response (pCR/MPR vs. non-MPR) for comparison. Median follow-up was 43.0 months (110 were evaluable), with 53 deaths and 46 remaining recurrence-free. Initial pathological N status and differentiation at recurrence were prognostic factors for both DFS and OS, with HRs of 2.29 (95

OBJECTIVES:Patients with recurrent tongue squamous cell carcinoma (RTSCC) receiving nonsurgical treatment have a poor prognosis. This study aims to identify independent factors associated with survival in these patients and to evaluate the effects of treatment response on survival outcomes. METHODS:Patients with RTSCC who received nonsurgical treatment at Hunan Cancer Hospital between January 2017 and July 2024 were retrospectively enrolled. Pretreatment metabolic parameters derived from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) were measured, including the maximum, mean, and peak standardized uptake values corrected for lean body mass (SULmax, SULmean, and SULpeak), the lesion-to-mediastinal blood-pool SULmean ratio (SULR), whole-body total metabolic tumor volume (MTV), and whole-body total lesion glycolysis (TLG). Baseline clinical characteristics, pathological features from the initial surgery, and treatment modalities after recurrence were also collected. Treatment response was evaluated according to the Response Evaluation Criteria in Solid Tumors, version 1.1 (RECIST 1.1), or the immune Response Evaluation Criteria in Solid Tumors (iRECIST), and patients were classified into response and nonresponse groups. Progression-free survival (PFS) and overall survival (OS) were followed. Univariate and multivariate Cox regression analyses were performed to identify independent factors associated with PFS and OS. Two sensitivity analyses, restricted to patients scanned using the same scanner model and to those who underwent whole-body scanning, respectively, were conducted to assess the robustness of the primary findings. Model performance was evaluated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and calibration curves. Time-dependent Cox regression and landmark analyses were used to assess the effects of treatment response on survival. RESULTS:A total of 93 patients with RTSCC were included. The follow-up duration ranged from 2 to 81 months, with a median of 38 months. Disease progression occurred in 72 patients, and 66 patients died. PFS ranged from 1 to 81 months, with a median of 4 months, whereas OS ranged from 2 to 81 months, with a median of 11 months. In the primary analysis, radiotherapy after recurrence was an independent protective factor for PFS (HR=0.461, 95% CI 0.233 to 0.912, P=0.026). This association remained significant in the sensitivity analysis restricted to patients scanned using the same scanner model (P=0.032), but was not significant in the analysis restricted to patients who underwent whole-body scanning (P=0.159). The corrected C-index of the PFS prediction model was 0.609, indicating limited predictive performance for 3- and 6-month PFS. In the analysis of OS, the natural logarithm of MTV [ln(MTV); HR=1.299, 95% CI 1.092 to 1.546, P=0.003] and radiotherapy after recurrence (HR=0.377, 95% CI 0.180 to 0.790, P=0.010) were independent predictors of OS. Both sensitivity analyses supported the robustness of these findings. The corrected C-index of the OS prediction model was 0.663, indicating moderate discrimination for 1- and 2-year OS. Time-dependent Cox regression analysis showed that treatment response was not significantly associated with PFS (HR=0.618, 95% CI 0.327 to 1.167, P=0.138; C-index=0.517), whereas treatment response had a significant protective effect on OS (HR=0.340, 95% CI 0.196 to 0.590, P<0.001; C-index=0.620). The 2-month landmark analysis showed that both PFS and OS were significantly longer in the response group than in the nonresponse group (both P<0.001). CONCLUSIONS:Whole-body total MTV and radiotherapy after recurrence have potential prognostic value for OS in patients with RTSCC receiving nonsurgical treatment. The protective association between radiotherapy after recurrence and PFS requires further validation. Patients who achieved a treatment response had better OS than those who did not respond.

OBJECTIVES:The Node Reporting and Data System (Node-RADS) offers a reliable framework for lymph node assessment, but its prognostic significance remains unexplored. This study aims to investigate the added prognostic value of Node-RADS in patients with locally advanced gastric cancer (LAGC) undergoing neoadjuvant chemotherapy (NAC) followed by gastrectomy. MATERIALS AND METHODS:This single-center retrospective study included 118 patients with LAGC underwent NAC and gastrectomy. The maximum Node-RADS score and the number of metastatic lymph node stations (defined as LNM-Station) were evaluated on pretreatment CT. The pretreatment Node-RADS-CT and Node-RADS-integrated models were developed using Cox regression to predict overall survival (OS) and disease-free survival (DFS). The pretreatment cN-CT models, cN-integrated models, as well as post-NAC pathological models were also developed in comparison. The performance of the models was assessed in terms of discrimination, calibration and clinical applicability. RESULTS:The LNM-Station was significantly associated with OS and DFS (all p < 0.05). The Node-RADS-CT model showed higher Harrell's consistency index (C-index) than cN-CT model (0.755 vs. 0.693 for OS, p = 0.017; 0.759 vs. 0.706 for DFS, p = 0.018). The Node-RADS-integrated model also achieved higher C-index than cN-integrated model (0.771 vs. 0.731 for OS, p = 0.091; 0.773 vs. 0.733 for DFS, p = 0.053). The net reclassification improvement (NRI) of the Node-RADS-integrated model at 5 years was 0.379 for OS and 0.364 for DFS (all p < 0.05). The integrated discrimination improvement (IDI) of the Node-RADS-integrated model was 0.103 for OS and 0.107 for DFS (all p < 0.05). The C-indices (OS: 0.745; DFS: 0.746) of pathological models were slightly lower than those of Node-RADS-based models (all p > 0.05). CONCLUSION:The baseline Node-RADS score and LNM-Station were effective prognostic indicators for LAGC. The pretreatment CT Node-RADS-based models can offer added prognostic value for LAGC, compared with clinical N stage.

The Node Reporting and Data System (Node-RADS) provides structured and effective evaluation for lymph nodes in malignancies. This study aims to investigate its value in predicting residual lymph node metastasis (LNM) and survival outcome of locally advanced gastric cancer (LAGC). This retrospective study included 118 patients with LAGC underwent neoadjuvant chemotherapy (NAC) and gastrectomy from April 2015 to June 2020. The diagnostic performance of the post-NAC CT-based Node-RADS score for regional LNM, both at the patient level and at the perigastric/extragastric subgroup level, was estimated using area under receiver operating characteristic curve (AUC) and Youden’s index. Kaplan-Meier curve was employed for prognostic analyses between high/low Node-RADS score group. A predictive Node-RADS (NR) model for LNM was developed using logistic regression analyses and a prognostic NR model for overall survival (OS) was developed using Cox regression analyses. In the prediction of LNM, the Node-RADS score exhibited an AUC of 0.843 (95

Background Accurate prediction of tumor response to neoadjuvant immunochemotherapy (NAIC) enables personalized perioperative therapy for resectable non-small cell lung cancer (NSCLC). Objective The present aimed to evaluate the predictive value of radiomics derived from the tumor-parenchyma invasive zone for response to NAIC in resectable NSCLC, with the goal of developing a more accurate and clinically applicable model. Methods Patients with pathologically proven NSCLC from August 2019 and March 2025 were retrospectively included from two medical centers. In the training set, radiomics features were extracted from the whole tumor region and tumor margin region (6mm) respectively. Following feature selection via intraclass correlation coefficient and least absolute shrinkage and selection operator, the Whole Tumor Model (WTM) and Tumor Margin model (TMM) were developed to non-invasively predict major pathological response (MPR) following NAIC. The performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value in the internal validation and external test sets. The optimal radiomics model and clinical characteristics were combined to build the hybrid model (HM). Results A total of 169 patients (median age, 60 years; 154 men) were divided into training, internal validation and external test sets, with 104 patients (61.5%) achieving MPR. In the test dataset, WTM and TMM achieved AUCs of 0.71 (95% CI: 0.54–0.89) and 0.84 (95% CI: 0.71–0.97), respectively. After incorporating tumor margin radiomics features and clinical predictors(pathology), the HM demonstrated satisfactory performance in the training set (AUC: 0.88, 95% CI: 0.81–0.95) and internal validation set (AUC: 0.86, 95% CI: 0.74–0.98). In the independent external test set, the HM obtained satisfactory performance (AUC = 0.87, 95% CI: 0.76–0.98). Decision curves analysis indicated that the radiomics-clinical combined nomogram provided significant clinical utility. Conclusion A radiomics model based on the tumor margin region outperformed the whole-tumor model in predicting MPR in NSCLC. Our study developed a novel tool to predict the response of NSCLC to NAIC, which demonstrated excellent performance.

BACKGROUND:Pulmonary sclerosing pneumocytoma (PSP) and pulmonary carcinoid (PC) are difficult to distinguish based on conventional imaging examinations. In recent years, radiomics has been used to discriminate benign from malignant pulmonary lesions. However, the value of radiomics based on computed tomography (CT) images to differentiate PSP from PC has not been well explored.PURPOSE:We aimed to investigate the feasibility of radiomics in the differentiation between PSP and PC.METHODS:Fifty-three PSP and fifty-five PC were retrospectively enrolled and then were randomly divided into the training and test sets. Univariate and multivariable logistic analyses were carried to select clinical predictor related to differential diagnosis of PSP and PC. A total of 1316 radiomics features were extracted from the unenhanced CT (UECT) and contrast-enhanced CT (CECT) images, respectively. The minimum redundancy maximum relevance and the least absolute shrinkage and selection operator were used to select the most significant radiomics features to construct radiomics models. The clinical predictor and radiomics features were integrated to develop combined models. Two senior radiologists independently categorized each patient into PSP or PC group based on traditional CT method. The performances of clinical, radiomics, and combined models in differentiating PSP from PC were investigated by the receiver operating characteristic (ROC) curve. The diagnostic performance was also compared between the combined models and radiologists.RESULTS:In regard to differentiating PSP from PC, the area under the curves (AUCs) of the clinical, radiomics, and combined models were 0.87, 0.96, and 0.99 in the training set UECT, and were 0.87, 0.97, and 0.98 in the training set CECT, respectively. The AUCs of the clinical, radiomics, and combined models were 0.84, 0.92, and 0.97 in the test set UECT, and were 0.84, 0.93, and 0.98 in the test set CECT, respectively. In regard to the differentiation between PSP and PC, the combined model was comparable to the radiomics model, but outperformed the clinical model and the two radiologists, whether in the test set UECT or CECT.CONCLUSIONS:Radiomics approaches show promise in distinguishing between PSP and PC. Moreover, the integration of clinical predictor (gender) has the potential to enhance the diagnostic performance even further.

OBJECTIVE:This article aimed to differentiate noncalcified hamartoma from pulmonary carcinoid preoperatively using computed tomography (CT) radiomics approaches.MATERIALS AND METHODS:The unenhanced CT (UECT) and contrast-enhanced CT (CECT) data of noncalcified hamartoma (n = 73) and pulmonary carcinoid (n = 54; typical/atypical carcinoid = 13/41) were retrospectively analyzed. The patients were randomly divided into the training and validation sets. A total of 396 radiomics features were extracted from UECT and CECT, respectively. The features were selected by using the minimum redundancy maximum relevance and the least absolute shrinkage and selection operator to construct a radiomics model. Clinical factors and radiomics features were integrated to build a nomogram model. The performance of clinical factors, radiomics, and nomogram models on the differential diagnosis between noncalcified hamartoma and carcinoid were investigated. Diagnostic performance of radiologists was also explored.RESULT:In regard to distinguishing noncalcified hamartoma from carcinoid, the areas under the receiver operating characteristic curves of the clinical, radiomics, and nomogram models were 0.88, 0.94, and 0.96 in the training set UECT, and were 0.85, 0.92, and 0.96 in the training set CECT, respectively. The areas under the curve of the 3 models were 0.89, 0.96, and 0.96 in the validation set UECT, and were 0.79, 0.90, and 0.94 in the validation set CECT, respectively. The nomogram model exhibited good calibration and was clinically useful by decision curve analysis. Nomogram did not show significant improvement compared with radiomics, neither for UECT nor for CECT. Diagnostic performance of radiologists was lower than both radiomics and nomogram model.CONCLUSIONS:Radiomics approaches may be useful in distinguishing peripheral pulmonary noncalcified hamartoma from carcinoid. Radiomics features extracted from CECT provided no significant benefit when compared with UECT.

ObjectiveTo explore the potential of CT radiomics in detecting acquired T790M mutation and predicting prognosis in patients with advanced lung adenocarcinoma with progression after first- or second-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) therapy.Materials and MethodsContrast-enhanced thoracic CT was collected from 250 lung adenocarcinoma patients (with acquired T790M mutation, n = 146, without mutation, n = 104) after progression on first- or second-generation TKIs. Radiomic features were extracted from each volume of interest. The maximum relevance minimum redundancy and the least absolute shrinkage and selection operator (LASSO) regression method were used to select the optimized features in detecting acquired T790M mutation. Univariate Cox regression and LASSO Cox regression were used to establish the radiomics model to predict the progression-free survival of osimertinib treatment. Finally, nomograms (which) combined clinical factors with radscore to predict the acquired T790M mutation and prognosis were built separately. In addition, the two nomograms were validated by the concordance index (C-index), decision curve analysis (DCA), and calibration curve analysis where appropriate.ResultsClinical factors including the progression-free survival of first-line EGFR TKIs, EGFR mutation, and N stage and 12 radiomic features were useful in predicting the acquired T790M mutation. The area under the receiver operating characteristic curves (AUC) of clinical, radiomics, and nomogram models were 0.70, 0.74, and 0.78 in the training set and 0.71, 0.71, and 0.76 in the validation set, respectively. The DCA and calibration curve analysis demonstrated a good performance of the nomogram model. Clinical factors including age and first-generation EGFR TKIs and 12 radiomic features were useful in patients’ outcome prediction. The C-index of the combined nomogram was 0.686 in the training set and 0.630 in the validation set, respectively. Calibration curves demonstrated a relatively poor performance of the nomogram model.ConclusionNomogram combined clinical factors with radiomic features might be helpful to detect whether patients developed acquired T790M mutation or not after progression on first- or second-generation EGFR TKIs. Nomogram prognostic model combined clinical factors with radiomic features might have a limited value in predicting the survival of patients harboring acquired T790M mutation treated with osimertinib.

目的:分析肺栓塞患者行多层螺旋CT诊断的临床应用价值.方法:2019年2月-2020年7月收治肺栓塞患者50例,随机分为两组,各25例.对照组实施X线片检查;试验组应用多层螺旋CT诊断,比较两组影像图像质量、扫描时间及两组肺栓塞检出率.结果:试验组影像图像质量与扫描时间均优于对照组,差异有统计学意义(P<0.05);试验组各项检出率均高于对照组,差异有统计学意义(P<0.05).结论:针对肺栓塞患者应用多层螺旋CT诊断,比X线片可显著提高临床检出率,提升图像质量等级,并大幅度减少扫描时间.

目的:分析对肺部孤立性球形病变应用CT检查与普放检查诊断的漏诊及误诊情况.方法:选取2018年5月-2020年10月经临床病理检查确诊为肺部孤立性球形病变的患者100例,分别采用CT检查与普放检查.对比两种检查诊断的漏诊及误诊情况,并记录诊断准确率.结果:CT检查的漏诊率及误诊率均低于普放检查,且CT检查的诊断准确率与确诊率均高于普放检查,差异有统计学意义(P<0.05).结论:针对肺部孤立性球形病变采取CT检查,可有效提高诊断准确率与确诊率,漏诊率及误诊率较低.

目的 探讨术前CT腹膜癌指数(PCI)作为无创性手段预测卵巢癌患者行卵巢肿瘤减灭术的效果的可行性及其与卵巢癌患者疾病无进展生存期(PFS)和总生存期(0S)之间的关系.方法 回顾性分析有术前全腹平扫+增强CT、治疗方式为直接手术且经病理证实为卵巢上皮性肿瘤患者,所有患者临床及影像资料完整,其临床随访截止时间为2020年12月30日.CT-PCI评分由两名放射科阅片者在不知道手术及病理结果的情况下分别进行回顾性阅片获得.手术PCI评分通过手术记录及病理资料获得.术前CT-PCI分数与手术PCI分数进行对照,计算Spearsrman相关系数.CT-PCI分数与初始肿瘤减灭术术后残留的关系用ROC曲线分析.CT-PCI与卵巢癌患者PFS及OS的关系用Kaplan-Meier生存曲线和多因素Cox回归分析进行.结果 共纳入病例150例.CT-PCI分数与术后有无残留相关(OR 1.633,95％置信区间1.421～1.878,P＜0.001).CT-PCI分数影响卵巢癌患者的PFS(HR 1.062,95％CI 1.015 ～1.112,P=0.009)及OS(HR 1.076,95％CI 1.018～1.137,P=0.009).上腹部腹膜受侵、小肠受侵是疾病PFS缩短的独立危险因素[HR =6.718(P ＜0.001),HR =9.763(P ＜0.001)];也是OS缩短的独立危险因素[HR =4.127(P=0.003),HR =7.480(P ＜0.001)].结论 术前CT-PCI分数与术后有无残留相关.术前CT-PCI分数与卵巢癌患者PFS和OS呈负相关.上腹部受侵和小肠肠管受侵或可作为卵巢癌患者PFS及OS的独立预后因素.

Rationale: Malignant hepatic epithelioid hemangioendotheliom (HEH) is a rare vascular tumor of endothelial origin, with multiple metastases to the spleen. This report describes a diffuse HEH with splenic metastasis on 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) images and delayed mutifocal bone metastasis after liver transplantation (LTx). Patient concerns: A 30-year-old male was admitted to our hospital with a complaint of abdominal distension, fatigue, and anorexia for 2 months. Diagnoses: Mild to moderate FDG uptake in the whole liver, and multifocal FDG uptake in the spleen were observed on 18F-FDG PET/CT scan. Ultrasound guided liver biopsy was performed, and a diagnosis of HEH was confirmed. Interventions: The patient underwent LTx and splenectomy. Outcomes: The patient developed low back pain due to unknown etiology, 3 months after surgery. A follow-up 18F-FDG PET/CT scan demonstrated multifocal bone destruction. Unfortunately, the patient died 12 months after surgery. Lessons: It is noteworthy that despite liver transplantation for the treatment of HEH, there may be a risk of recurrence. For these patients with extrahepatic lesions, adjuvant chemotherapy may be a useful alternative treatment method for the prevention of recurrence.

Peritoneum consists of a single layer of mesothelium and a small amount of connective tissue. Peritoneal diseases mainly include peritoneal tumors and inflammatory lesions. Peritoneal tumors can be subdivided into primary peritoneal tumors and secondary peritoneal tumors. The incidence of secondary peritoneal tumors is far higher than that of primary peritoneal tumors. Secondary peritoneal tumors include peritoneal carcinomatosis, peritoneal pseudomyxoma, peritoneal lymphomatosis and peritoneal sarcomatosis. Summarizing the imaging features of secondary peritoneum tumors and the related research progress can help radiologists improve the diagnostic accuracy of peritoneal secondary tumors and guide clinical diagnosis and treatment. Ultrasound, CT, MR and PET/CT imaging features of peritoneal secondary tumors were reviewed.

Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) is widely applied in non-small cell lung cancer (NSCLC). The standardized uptake value (SUV), a semi-quantitative index, plays an essential role in NSCLC for diagnosis, staging, and efficacy evaluation. It has been proposed that the SUVmax of tumors may correlate with the presence or absence of chemotherapy resistance-associated biomarkers based on studies that have displayed a close correlation between SUVmax and the expression levels of excision repair cross-complementary Group 1 (ERCC1)[1] and Tp53-induced glycolysis and apoptosis regulator.[2] FDG avidity of NSCLC and ERCC1 and ribonucleotide reductase subunit M1 (RRM1) levels have not been as extensively investigated. Based on these findings, we looked for correlations among metabolic parameters (SUVmax, metabolic tumor volume [MTV], and total lesion glycolysis [TLG]) and ERCC1 and RRM1 expression in patients with NSCLC, to investigate whether FDG uptake reflects the presence or absence of chemoresistance proteins (ERCC1 and RRM1) within tumor cells. The study was approved by the Medical Ethics Committee of The Second Xiangya Hospital, Central South University, China. All individuals signed written informed consent. The cases of 89 patients with curatively resected Stage I or II adenocarcinoma or squamous cell NSCLC, for which paraffin-embedded tumor specimens and preoperative PET/CT scans were available, were enrolled. SUVmax, MTV, and TLG were calculated. PET/CT images were analyzed by experienced nuclear physicians blinded to the clinical history using a workstation (MEMRS Workstation 6.6, MedEX, Beijing, China). The maximum diameter of the tumor was measured on CT mediastinal windows. The SUVmax was automatically calculated by drawing regions of interest around the lung cancer nodules on the fused images. MTV was calculated using a fixed threshold SUV of 2.5. TLG was calculated as the product of MTV multiply SUVmean. Nuclear (ERCC1) and cytoplasmic (RRM1) expression of the biomarkers was measured with immunohistochemical staining. All immunostained sections were reviewed under a light microscope (Olympus, Japan) at ×400 magnification by an experienced pathologist, looking for cytoplasmic staining of RRM1 and nuclear staining of ERCC1. The ERCC1 and RRM1 concentrations in each case were estimated by a semi-quantitative H-score according to the intensity of staining. Spearman's correlation analysis was employed to confirm the relationship between different parameters. Binary logistic regression analysis with the optimum metabolic parameters was used to predict ERCC1 and/or RRM1 expression. Receiver operating characteristic (ROC) curves of the PET/CT metabolic parameters (SUVmax, MTV, and TLG) for the prediction of ERCC1 and/or RRM1 expression were generated to ascertain the cutoff value which yielded a maximum sensitivity and specificity. P < 0.05 indicated a statistically significant difference. Of the 89 cases, 67 (75.3%) were ERCC1 positive and 63 (70.8%) were RRM1 positive [Figure 1a]. No significant correlation was found between SUVmax, MTV, or TLG and the expression of ERCC1 (P = 0.135, 0.170, and 0.422). RRM1 expression correlated negatively with SUVmax (r = −0.360, P = 0.001), MTV (r = −0.290, P = 0.006), and TLG (r = −0.315, P = 0.003). Binary logistic regression analysis revealed SUVmax to be the optimal index reflecting the expression of RRM1. ROC curve analysis revealed that the area under curve is 0.719 with 95% confidence interval ranging from 0.591 to 0.846 (P = 0.001). ROC also demonstrated that the optimal cutoff value of SUVmax to predict RRM1 negativity was 10.2, which was associated with 73.1% sensitivity and 68.3% specificity [Figure 1b].Figure 1: (a) Representative immunohistochemistry images: immunohistochemical stains for (A, negative; B, positive) ERCC1 and (C, negative; D, positive) RRM1 (original magnification ×400). (b) ROC curves of the SUVmax for the prediction of RRM1 expression. Area under the curve: 0.719; 95% CI: 0.591–0.846; P = 0.001. A SUVmax ratio of 10.2 or lower suggests a NSCLC to be RRM1 negative with a sensitivity of 73.1% and specificity of 68.3%. ERCC1: Excision repair cross-complementary Group 1; RRM1: Ribonucleotide reductase subunit M1; CI: Confidence interval; SUVmax: Maximum standardized uptake value; NSCLC: Non-small cell lung cancer.This study evaluated the correlation of two tumor markers of chemotherapy resistance in NSCLC with FDG PET/CT. Duan et al.[3] suggested a correlation between SUVmax and ERCC1 levels in NSLCLC, but when they used multiple stepwise regression analysis detected no robust correlation, so based on their study, it is still inconclusive whether SUVmax can be applied to determine ERCC1-related chemotherapy resistance. Our results were similar to their study. Our data also did not find any significant difference in FDG avidity between the ERCC1-positive and ERCC1-negative groups. In another study, their results showed that ERCC1 expression had a statistically significant relationship with the degree of FDG uptake in thymic epithelial tumors (especially thymoma).[4] Further larger studies may be needed to confirm whether the FDG avidity of NSCLC or other tumors can be used as an indicator of ERCC1 expression. RRM1 can predict and monitor the response of NSCLC patients to gemcitabine.[5] A feature of our study was first analyzing the correlation between RRM1 expression and the degree of FDG avidity of NSCLC. Our results displayed that RRM1 expression is negatively correlated with tumor SUVmax, MTV, and TLG in patients with NSCLC. The optimal cutoff value of SUVmax was approximately 10.1. However, the results have only 73.1% sensitivity and 68.3% specificity and have not been correlated with clinical outcome. According to our findings, 18F-FDG PET/CT might serve as a simple and practical noninvasive method for predicting RRM1 expression in NSCLCs, allowing the tailoring of chemotherapy to exclude gemcitabine if expression is elevated. Larger sample bench studies and clinical trials need to assess these findings' clinical applicability. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

OBJECTIVE To review the dual-time-point 18F-fluorodeoxyglucose (FDG) positron emission tomography/X-ray computed tomography (PET-CT) imaging for a patient with primary ureteric lymphoma (PUL), and explore the potential of FDG PET-CT on diagnosis, staging and evaluation of treatment in patients with PUL.
 Methods: Complete clinical and imaging data of one patient with PUL was reported. The relevant literatures from 1997 to 2016 were reviewed, and the imaging features of uriteric lymphoma were analyzed.
 Results: The patient presented with microhematuria, a soft-density mass with moderate enhancement in the mid left ureter, and a luminal stenosis with the scope inserted 5 cm into the left stoma, but no mass was found. The patient was pathologically diagnosed as follicular lymphoma (stage IE confirmed by whole body FDG PET-CT).
 Conclusion: PET-CT may be useful in diagnosis, clinical stage and therapy assessment in patients with PUL.

[目的]探讨正电子发射计算机断层显像(PET-CT)对腹膜假性黏液瘤(PMP)诊断价值.[方法]回顾性分析本院收治的1例经手术证实的PMP患者的PET-CT影像特点,根据其最大18F-FDG标准吸收值(SUV-max)及其影像特点总结其诊断经验.[结果]PET-CT图像显示腹、盆腔内大量水样密度影,CT值范围约15～19HU,未见明显糖代谢增高.SUVmax为0.86,增强扫描“腹水”未见明显异常强化;改变体位(右侧卧位)检查腹腔内水样密度影形态变化不明显,腹腔肠管未见明显活动;肝脏边缘呈扇贝形受压,肠管受压、内移;网膜、肠系膜未见明显增厚,腹腔内未见明显肿大淋巴结,综上考虑为PMP可能性大,术后病理证实为低级别PMP.[结论]PMP较罕见,PET-CT对该病有较好的诊断价值.