BACKGROUND:There is much controversy on the extent of lymph node dissection (LND) for papillary thyroid carcinoma (PTC) patients with lymph node metastasis (LNM) according to major guidelines. This study aims to explore the optimal extent of LND and discover a personalized and accurate surgical plan for pN1 PTC patients. METHODS:This prospective study included 550 patients with PTC who underwent initial surgery. For patients who were considered pN1a, LND of levels III and IV was completed. For patients who were considered LNM in levels III or IV, after taking 3-6 lymph nodes of level III for frozen pathological examination, if LNM was found, LND of levels II-IV was performed; otherwise, only levels III and IV were dissected, For patients who were considered to have LNM in level II, LND of levels II-V was performed. Statistical analysis was performed using SPSS software. RESULTS:51.4% of patients with pN1a had postoperative pathologically confirmed occult LNM in levels III and IV. Among patients who underwent LND of levels II-IV due to positive lymph nodes in level III, 46.1% had occult LNM in level II. For patients with LNM in level II, the incidence of occult metastasis in level V was 20%. Only one patient presented with lymph node recurrence outside of the operative field. The proportion of patients with postoperative complications increased as the scope of dissection enlarged. CONCLUSION:Combined with the distribution of LNM and the number of subregions of LNM(n) in preoperative ultrasonography, it is suggested that the range of LND in the lateral neck of pN1 PTC should be n + 1/2 anatomical subregions.

Background: Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy, with lymph node metastasis (LNM) being a key predictor of recurrence and poor prognosis. Preoperative detection of LNM remains challenging due to the limitations of imaging modalities, leading to inadequate surgical resection in 20-30% of patients. While immune checkpoint molecules have been implicated in PTC progression, the heterogeneity of CD8+ T cell exhaustion subsets and their specific association with LNM remain poorly defined. Herein, we aimed to characterize the distinct immune landscape of PTC prone to LNM, with a focus on terminal immune exhaustion, to improve risk stratification and therapeutic strategies. Methods: Fresh PTC tissues from 40 patients (22 LNM-positive, 18 LNM-negative) were analyzed by flow cytometry (FCM) to quantify immune cell subsets, inflammatory cytokines, and chemokines. Immunohistochemistry (IHC) validated CD45+ immune cell infiltration. Transcriptomic and clinical data from 448 PTC patients in The Cancer Genome Atlas (TCGA-PTC) cohort were used for bioinformatic validation, including Gene Set Variation Analysis (GSVA) of terminal exhaustion gene signatures. Results: LNM-positive PTC exhibited a unique inflammatory milieu with significantly elevated IL-6, IL-1ra, CCL5, and IL-9 levels (all p<0.05) in tumor interstitial fluid. FCM analysis revealed that LNM-positive PTC had increased infiltration of total CD45+ immune cells, CD3+ T cells, and CD3+CD8+ T cells (all p<0.05). Critically, terminally exhausted PD-1hiTIM-3+ CD8+ T cells were significantly enriched in LNM-positive PTC (p=0.022) and positively correlated with extrathyroidal extension (p=0.044). Additionally, LNM risk was associated with increased CD4+ regulatory T (Treg) cell frequency (p=0.023) and elevated CTLA-4 expression on CD4+ T cells (p=0.047). In TCGA-PTC validation, the terminal exhaustion gene signature was predominantly enriched in LNM-positive (p<0.0001) and advanced-stage PTC (p<0.001), and strongly correlated with BRAF V600E mutation (p<0.0001)—the most common oncogenic driver in aggressive PTC. Conclusion: Our findings identify a terminal immune exhaustion phenotype (characterized by PD-1hiTIM-3+ CD8+ T cells and Treg enrichment) as a key feature of LNM-prone PTC. This phenotype is conserved across clinical samples and TCGA datasets, linking BRAF V600E mutation to immune suppression and metastatic potential. These insights provide a novel immune-based biomarker for LNM risk stratification and support the potential of combining anti-PD-1/TIM-3 therapy with BRAF inhibitors for high-risk PTC.

BACKGROUND:The right innominate interarteriovenous lymph nodes (RIAVLN) represent a distinct nodal group situated between the right innominate artery and vein. This area lies outside the conventional level VI-VII boundaries in thyroid carcinoma surgery and is seldom addressed in standard guidelines. Metastasis in this region is difficult to detect and surgically challenging due to its proximity to major vascular structures. This study aimed to analyze the clinical characteristics and surgical management of RIAVLN metastasis in patients with thyroid carcinoma. METHODS:We retrospectively reviewed 103 patients with thyroid carcinoma who underwent RIAVLN dissection between July 2017 and January 2024. All patients had preoperative contrast-enhanced CT scans suggesting nodal metastasis in this region. Demographic data, tumor subtype, surgical approach, and pathological findings were analyzed. The surgical technique emphasized cervical exposure of the carotid sheath, mobilization of the common carotid artery, and careful dissection of the interarteriovenous space, with partial sternotomy reserved for cases with severe adhesion or bleeding risk. RESULTS:The mean patient age was 39.9 ± 12.3 years (range, 18-68), with 42 males and 61 females. The cohort included 24 primary papillary, 63 recurrent papillary, 2 primary medullary, and 13 recurrent medullary thyroid carcinoma cases. Overall, 93 patients (90.3%) were successfully treated via a transcervical approach, while 10 (9.7%) required partial sternotomy. The mean number of RIAVLN dissected was 3.1 ± 2.9, and metastasis was confirmed in 77 patients (74.8%). The mean number of metastatic nodes among positive cases was 1.5 ± 2.4, with extranodal extension observed in 12 patients (11.7%). No major vascular injury or operative mortality occurred. CONCLUSIONS:RIAVLN metastasis is relatively common in recurrent thyroid carcinoma and represents an anatomically unique nodal group not covered by traditional classifications. In most cases, complete clearance can be safely achieved through a transcervical approach. Partial sternotomy should be reserved for patients with dense adhesions or high bleeding risk. Recognition of this region as a potential site of recurrence and mastery of its surgical anatomy are crucial for achieving optimal oncologic outcomes in thyroid cancer surgery.

Background: Lipid metabolic reprogramming is a hallmark of tumor progression, yet the role of low-density lipoprotein (LDL) metabolism in head and neck squamous cell carcinoma (HNSCC) remains unclear. This study aimed to systematically characterize LDL metabolism and identify clinically relevant regulators. Methods: Integrated multi-omics analyses were performed using TCGA and GEO cohorts. Single-cell RNA sequencing was applied to resolve cell-type–specific metabolic heterogeneity. A machine learning–based prognostic model was developed and validated across independent datasets. Functional analyses, immune profiling, and tumor mutation analyses were conducted. Key genes were further validated using spatial transcriptomics and in vitro experiments. Results: LDL metabolic activity exhibited marked heterogeneity, with myeloid cells—particularly macrophages—showing the highest activity. The LDL-related prognostic model effectively stratified patients by survival and immunotherapy response. High-risk patients displayed elevated tumor mutational burden, reduced immune infiltration, and poorer response to immune checkpoint blockade. Notably, the macro_SPP1 macrophage subset exhibited enhanced LDL metabolism and extracellular matrix activity, along with upregulated ITGA5. Integrated analyses indicated that ITGA5 is associated with cholesterol metabolism, immune regulation, and macrophage differentiation. Spatial transcriptomics confirmed its enrichment in tumor regions with high LDL activity. Functional experiments demonstrated that ITGA5 promotes proliferation, migration, and invasion of HNSCC cells. Conclusion: LDL metabolic reprogramming shapes the tumor microenvironment and clinical outcomes in HNSCC. ITGA5 acts as a key mediator linking lipid metabolism to malignant progression, representing a potential prognostic biomarker and therapeutic target.

Introduction Studies have revealed that age is associated with the risk of lateral lymph node metastasis (LLNM) in papillary thyroid cancer (PTC). This study aimed to identify the optimal cut point of age for a more precise prediction model of LLNM and to reveal differences in risk factors between patients of distinct age stages. Methods A total of 499 patients who had undergone thyroidectomy and lateral neck dissection (LND) for PTC were enrolled. The locally weighted scatterplot smoothing (LOWESS) curve and the ‘changepoint’ package were used to identify the optimal age cut point using R. Multivariate logistic regression analysis was performed to identify independent risk factors of LLNM in each group divided by age. Results Younger patients were more likely to have LLNM, and the optimal cut points of age to stratify the risk of LLNM were 30 and 45 years old. Central lymph node metastasis (CLNM) was a prominent risk factor for further LNM in all patients. Apart from CLNM, sex( p = 0.033), tumor size( p = 0.027), and tumor location( p = 0.020) were independent predictors for patients younger than 30 years old; tumor location( p = 0.013), extra-thyroidal extension( p < 0.001), and extra-nodal extension( p = 0.042) were independent risk factors for patients older than 45 years old. Conclusions Our study could be interpreted as an implication for a change in surgical management. LND should be more actively performed when CLNM is confirmed; for younger patients with tumors in the upper lobe and older patients with extra-thyroidal extension tumors, more aggressive detection of the lateral neck might be considered.

BACKGROUND:Jugulo-omohyoid lymph nodes (JOHLN) metastasis has proven to be associated with lateral lymph node metastasis (LLNM). This study aimed to reveal the clinical features and evaluate the predictive value of JOHLN in PTC to guide the extent of surgery.METHODS:A total of 550 patients pathologically diagnosed with PTC between October 2015 and January 2020, all of whom underwent thyroidectomy and lateral lymph node dissection, were included in this study.RESULTS:Thyroiditis, tumor location, tumor size, extra-thyroidal extension, extra-nodal extension, central lymph node metastasis (CLNM), and LLMM were associated with JOHLN. Male, upper lobe tumor, multifocality, extra-nodal extension, CLNM, and JOHLN metastasis were independent risk factors from LLNM. A nomogram based on predictors performed well. Nerve invasion contributed the most to the prediction model, followed by JOHLN metastasis. The area under the curve (AUC) was 0.855, and the p-value of the Hosmer-Lemeshow goodness of fit test was 0.18. Decision curve analysis showed that the nomogram was clinically helpful.CONCLUSION:JOLHN metastasis could be a clinically sensitive predictor of further LLM. A high-performance nomogram was established, which can provide an individual risk assessment of LNM and guide treatment decisions for patients.

Objective:To evaluate artificial intelligence constructed by deep convolutional neural network (DCNN) for the site identification in upper gastrointestinal endoscopy.Methods:A total of 21 310 images of esophagogastroduodenoscopy from the Cancer Hospital of Chinese Academy of Medical Sciences from January 2019 to June 2021 were collected. A total of 19 191 images of them were used to construct site identification model, and the remaining 2 119 images were used for verification. The performance differences of two models constructed by DCCN in the identification of 30 sites of the upper digestive tract were compared. One model was the traditional ResNetV2 model constructed by Inception-ResNetV2 (ResNetV2), the other was a hybrid neural network RESENet model constructed by Inception-ResNetV2 and Squeeze-Excitation Networks (RESENet). The main indices were the accuracy, the sensitivity, the specificity, positive predictive value (PPV) and negative predictive value (NPV).Results:The accuracy, the sensitivity, the specificity, PPV and NPV of ResNetV2 model in the identification of 30 sites of the upper digestive tract were 94.62%-99.10%, 30.61%-100.00%, 96.07%-99.56%, 42.26%-86.44% and 97.13%-99.75%, respectively. The corresponding values of RESENet model were 98.08%-99.95%, 92.86%-100.00%, 98.51%-100.00%, 74.51%-100.00% and 98.85%-100.00%, respectively. The mean accuracy, mean sensitivity, mean specificity, mean PPV and mean NPV of ResNetV2 model were 97.60%, 75.58%, 98.75%, 63.44% and 98.76%, respectively. The corresponding values of RESENet model were 99.34% ( P<0.001), 99.57% ( P<0.001), 99.66% ( P<0.001), 90.20% ( P<0.001) and 99.66% ( P<0.001). Conclusion:Compared with the traditional ResNetV2 model, the artificial intelligence-assisted site identification model constructed by RESENNet, a hybrid neural network, shows significantly improved performance. This model can be used to monitor the integrity of the esophagogastroduodenoscopic procedures and is expected to become an important assistant for standardizing and improving quality of the procedures, as well as an significant tool for quality control of esophagogastroduodenoscopy.

Objective The study aimed to investigate the minimal number of examined lymph nodes (ELNs) for accurate assessment of lymph node status and favorable prognosis in patients with stage T1-2 supraglottic laryngeal squamous cell carcinoma (LSCC) who received radical resection. Methods Patients with stage T1-2 supraglottic LSCC from the Surveillance, Epidemiology, and End Results (SEER) database and the Chinese Academy of Medical Sciences, Cancer Hospital/National Cancer Center (NCC) were reviewed. The association of the ELN count with the identification of nodal metastasis and overall survival (OS) was analyzed using a multivariate regression model. Locally weighted scatterplot smoothing fitting curve and the 'changepoint' package were adopted to identify the optimal cut points using R. Results A total of 429 patients from the SEER database and 53 patients from NCC were enrolled. The probability of identifying nodal metastasis was positively related to the ELN count. For patients diagnosed with pathological stage N0 (pN0) disease, the mortality risks rapidly decreased when the amount of ELNs exceeded ten, and those with ELNs >10 had better OS. Conclusion An adequate amount of ELNs benefits precise nodal staging in patients with stage T1-2 supraglottic LSCC. Ten lymph nodes are the minimum number of ELNs. For pN0 patients, an ELN count ≤10 is an unfavorable prognostic factor.

The study aimed to identify the value and optimal age cutoff to predict the progression of highly suspicious thyroid nodules ≤ 10 mm during active surveillance (AS), and to reveal distinct risk factors in patients of different ages. A total of 779 patients with highly suspicious thyroid nodules were enrolled and followed up by ultrasonography. Locally weighted scatterplot smoothing (LOWESS) and the package ‘changepoint’ were used to identify the optimal age cutoffs using R. Multivariate Cox regression was performed to identify independent prognostic factors in each patient group divided according to age. Age was an independent predictor of nodule progression (P = 0.038). The optimal age cutoff to stratify the risk of nodule progression was 30 years. Younger patients were more likely to have progression of nodules during AS (P < 0.001), including enlargement of nodule size (P = 0.011) and new lesion occurrence (P < 0.001). Nodule size was identified as a risk factor for disease progression in patients younger than 30 years old (P = 0.008, OR 7.946, 95

Objective:To explore the automatic recognition and classification of 20 anatomical sites in laryngoscopy by an artificial intelligence（AI） quality control system using convolutional neural network（CNN）. Methods: Laryngoscopic image data archived from laryngoscopy examinations at the Department of Endoscopy, Cancer Hospital, Chinese Academy of Medical Sciences from January to December 2018 were collected retrospectively, and a CNN model was constructed using Inception-ResNet-V2+SENet. Using 14000 electronic laryngoscope images as the training set, these images were classified into 20 specific anatomical sites including the whole head and neck, and their performance was tested by 2000 laryngoscope images and 10 laryngoscope videos. Results:The average time of the trained CNN model for recognition of each laryngoscopic image was（20.59 ± 1.55） ms, and the overall accuracy of recognition of 20 anatomical sites in laryngoscopic images was 97.75%（1955/2000）, with average sensitivity, specificity, positive predictive value, and negative predictive value of 100%, 99.88%, 97.76%, and 99.88%, respectively. The model had an accuracy of ≥ 99% for the identification of 20 anatomical sites in laryngoscopic videos. Conclusion:This study confirms that the CNN-based AI system can perform accurate and fast classification and identification of anatomical sites in laryngoscopic pictures and videos, which can be used for quality control of photo documentation in laryngoscopy and shows potential application in monitoring the performance of laryngoscopy.

Objective To construct and validate a deep convolutional neural network (DCNN)‐based artificial intelligence (AI) system for the detection of nasopharyngeal carcinoma (NPC) using archived nasopharyngoscopic images. Methods We retrospectively collected 14107 nasopharyngoscopic images (7108 NPCs and 6999 noncancers) to construct a DCNN model and prepared a validation dataset containing 3501 images (1744 NPCs and 1757 noncancers) from a single center between January 2009 and December 2020. The DCNN model was established using the You Only Look Once (YOLOv5) architecture. Four otolaryngologists were asked to review the images of the validation set to benchmark the DCNN model performance. Results The DCNN model analyzed the 3501 images in 69.35 s. For the validation dataset, the precision, recall, accuracy, and F1 score of the DCNN model in the detection of NPCs on white light imaging (WLI) and narrow band imaging (NBI) were 0.845 ± 0.038, 0.942 ± 0.021, 0.920 ± 0.024, and 0.890 ± 0.045, and 0.895 ± 0.045, 0.941 ± 0.018, and 0.975 ± 0.013, 0.918 ± 0.036, respectively. The diagnostic outcome of the DCNN model on WLI and NBI images was significantly higher than that of two junior otolaryngologists ( p < 0.05). Conclusion The DCNN model showed better diagnostic outcomes for NPCs than those of junior otolaryngologists. Therefore, it could assist them in improving their diagnostic level and reducing missed diagnoses. Level of Evidence 3 Laryngoscope , 134:127–135, 2024

Improving the preoperative diagnosis of cervical lymph node metastasis (LNM) will help improve the clinical outcomes of papillary thyroid carcinoma (PTC) patients. B7‐H3, as an immune checkpoint of the B7 family, is highly expressed in PTC tissues and related to LNM and prognosis. We aimed to explore the clinical values of serum B7‐H3 (sB7‐H3) in predicting LNM in PTC by a nomogram prediction model.

Objective: We evaluated the utility of concurrent chemoradiotherapy (CCRT) compared to surgery followed by adjuvant radiotherapy (with or without concurrent chemotherapy) (SRT) in terms of improving the life expectancy and quality-of-life (QOL) of patients with stage III/IV hypopharyngeal squamous cell carcinomas (HPSCCs).Methods: From January 2010 to July 2018, a total of 299 patients with stage III/IV HPSCC who received surgery followed by adjuvant radiotherapy (with or without concurrent chemotherapy) (SRT, n =111), or concurrent chemoradiotherapy (CCRT, n = 188) in our hospital were included. We measured overall survival (OS) and disease-free survival (DFS). We used the EORTC QLQ-C30, QLQH&N35, and Voice handicap index-30 (VHI-30) instruments to assess the longterm QOL.Results: The OS and DFS afforded by SRT were significantly better than those associated with CCRT (p = 0.039; p =0.048 respectively), especially for stage N2-N3 patients. CCRT patients experienced better speech outcomes.Conclusion: For resectable stage III/IV HPSCC patients, appropriate treatment plans should be selected comprehensively considering survival rate, QOL, patient preference and multidisciplinary treatment.(c) 2023 Asian Surgical Association and Taiwan Robotic Surgery Association. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Objective: The study aimed to determine the optimal count of examined lymph nodes (ELN) for accurate assessment of lymph node status and favorable long-term survival in patients with oral tongue squamous cell carcinoma (OTSCC) who received radical resection. Methods: Patients with OTSCC who received radical resection between 2004 and 2015 were enrolled from the Surveillance, Epidemiology, and End Results database (SEER) and were randomly divided into two cohorts. The association of ELN count with nodal migration and overall survival (OS) was analyzed using a multivariate regression model with the adjustment of relevant factors. Locally weighted scatterplot smoothing (LOWESS) and 'strucchange' package were adopted to identify the optimal cut points using R. Results: A total of 2077 patients were included in this study. The optimal cut points of ELN count for accurate nodal staging and favorable OS were 19 and 15, respectively. The probability of detecting positive lymph nodes (PLN) significantly increased in patients with ELN count >= 19 in comparison to those with ELN count < 19 (training set, P < 0.001; validation set, P=0.012). A better postoperative prognosis was observed in patients with ELN count >= 15 than those with fewer ELN (training set, P=0.001, OR: 0.765; validation set, P=0.016, OR: 0.678).Conclusion: The optimal cut point of ELN count to ensure the accuracy of nodal staging and to achieve a favorable postoperative prognosis were 19 and 15, respectively. The ELN count beyond the cutoff values might improve the accuracy of cancer staging and OS.

OBJECTIVES Video laryngoscopy is an important diagnostic tool for head and neck cancers. The artificial intelligence (AI) system has been shown to monitor blind spots during esophagogastroduodenoscopy. This study aimed to test the performance of AI-driven intelligent laryngoscopy monitoring assistant (ILMA) for landmark anatomical sites identification on laryngoscopic images and videos based on a convolutional neural network (CNN). MATERIALS AND METHODS The laryngoscopic images taken from January to December 2018 were retrospectively collected, and ILMA was developed using the CNN model of Inception-ResNet-v2 + Squeeze-and-Excitation Networks (SENet). A total of 16,000 laryngoscopic images were used for training. These were assigned to 20 landmark anatomical sites covering six major head and neck regions. In addition, the performance of ILMA in identifying anatomical sites was validated using 4000 laryngoscopic images and 25 videos provided by five other tertiary hospitals. RESULTS ILMA identified the 20 anatomical sites on the laryngoscopic images with a total accuracy of 97.60 %, and the average sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 100 %, 99.87 %, 97.65 %, and 99.87 %, respectively. In addition, multicenter clinical verification displayed that the accuracy of ILMA in identifying the 20 targeted anatomical sites in 25 laryngoscopic videos from five hospitals was ≥95 %. CONCLUSION The proposed CNN-based ILMA model can rapidly and accurately identify the anatomical sites on laryngoscopic images. The model can reflect the coverage of anatomical regions of the head and neck by laryngoscopy, showing application potential in improving the quality of laryngoscopy.

Objective:To explore the value of multi-parametric MRI for thyroid gland in differentiating benign and malignant thyroid nodules.Methods:From December 2018 to May 2020, 78 patients with 91 post-surgically pathologically confirmed thyroid nodules were enrolled in Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. For each patient, the following MRI findings were obtained including the nodules′ location, size, shape, margin, signal intensity, cystic change, degree and pattern of contrast enhancement, involvement of surrounding structure, and ADC values. The time-intensity curve (TIC) were plotted and subtyped based on dynamic contrast enhancement MRI. The MRI findings between the benign and malignant thyroid nodules were compared using Mann-Whitney U test, χ 2 test or Fisher exact test. Multiple logistic regression analysis was used to select independent predictive variables and build a combined model, and the ROC curve was used to evaluate the diagnostic performance of each MRI finding and the combined model. Results:Between the benign and malignant thyroid nodules, the significant differences were found in size, shape, margin, presence of cystic changes, T 1WI signal intensity, ADC value, enhancement homogeneity, TIC subtypes and presence of thyroid capsule involvement ( P<0.05). Multivariate logistic analysis showed that ill-defined margin (OR=77.61), no presence of cystic changes (OR=36.11) and difference between TIC subtypes (OR=83.41) were independent predictive variables, and the area under the ROC curve (AUC) was 0.879, 0.788, and 0.751, respectively. The AUC, sensitivity and specificity of the combined model were 0.977, 0.986, and 0.904, respectively. Conclusions:Thyroid multi-parametric MRI derived findings can be used for the differential diagnosis of benign and malignant nodules. Combined with the independent risk factors with ill-defined margin, no presence of cystic changes, TIC of type plateau or washout, the diagnostic model has a higher diagnostic efficiency.

食管癌及胃癌位列中国癌症发病率的前5位,大部分患者发现时即为中晚期,5年生存率较低,严重威胁患者的生命健康.食管癌和胃癌的早诊早治可明显降低患者的死亡率,提高生存率.内镜检查不仅是早期上消化道癌前病变及早期癌诊断的关键,而且还可完整切除病灶,达到治愈目的.伴随着食管癌及胃癌早诊早治理念的不断深入,进行胃镜诊疗的患者数量也在不断增加,但在应对检查例数增加的同时,缺乏有效的监测与监管及客观的评价,胃镜诊疗质量仍不容乐观.人工智能(artificial intelligence,AI)的出现将在胃镜检查中实时监测并提供智能预警,促进并监督内镜医师规范化操作,提高检查和操作质量.本文阐述了AI在胃镜质控中的应用进展和可能面临的问题.

目的 探讨采用甲状腺专用表面线圈的术前多参数MRI特征对甲状腺癌区域淋巴结转移的预测价值.材料与方法 回顾性分析中国医学科学院肿瘤医院深圳医院经手术病理证实的甲状腺癌51例,均行甲状腺病灶切除及颈部淋巴结清扫,术前均行3.0 T增强MRI扫描.以术后病理为对照,分析比较转移淋巴结和非转移淋巴结的MRI征象,构建转移淋巴结的预测模型.计量资料的比较采用t检验或U检验,计数资料比较采用χ2检验,多元逻辑回归分析用于构建预测模型,ROC曲线用于评估预测模型的诊断效能.结果 51例甲状腺癌患者共135枚淋巴结纳入研究,其中转移淋巴结74枚,非转移淋巴结61枚.转移与非转移淋巴结的长短径、ADC值、T1WI和抑脂T2WI信号、形状、边缘、是否囊变、强化程度等差异具有统计学意义(P均<0.05).其中抑脂T2WI呈混杂信号、明显强化、低ADC值(<0.91×10-3 mm2/s)是转移淋巴结的独立预测因子,联合这3个MRI征象所建立的预测模型其AUC为0.93,敏感度为82.4％,特异度为88.5％.甲状腺癌伴有桥本氏甲状腺炎时,其Ⅵ区非转移淋巴结的长径、短径均大于不伴桥本氏甲状腺炎组(P<0.05).结论 术前多参数MRI特征对甲状腺癌区域淋巴结转移有较高的预测价值,转移淋巴结多表现为抑脂T2WI呈混杂信号、明显强化、更低的ADC值,为甲状腺癌的术前临床决策提供非常有价值的依据.

目的探讨动态对比增强磁共振成像(dynamic contrast-enhanced MRI,DCE-MRI)的定量及半定量参数在鉴别甲状腺良恶性结节中的诊断效能.材料与方法回顾性分析经手术病理证实的甲状腺结节患者45例,术前均行颈部DCE-MRI增强检查,获取定量参数:容积转运常数(volume transfer constant,Ktrans)、速率常数(rate constant,Kep)、血管外细胞外容积分数(extravascular extracellular volume fraction,Ve)和非定量参数:增强曲线下初始面积(initial area under the gadolinium curve,IAUGC)、最大增强斜率(maximum slope of increase,Max Slope)、对比增强比率(contrast enhancement rate,CER)以及时间-信号曲线(time-intensity curves,TIC);应用Mann-Whitney U检验及Fisher检验分析两组间连续变量和分类变量是否有统计学差异,并通过绘制ROC曲线来评价DCE-MRI中的各参数在鉴别甲状腺良恶性结节方面的诊断效能.结果在45例甲状腺结节患者中,良性结节12个(良性组),恶性结节34个(恶性组),良性结节:平均Kep、Ktrans、Ve值分别为(3.63±2.83)min?1、(1.41±1.01)min?1、(0.48±0.19)min?1;恶性结节分别为(2.70±2.42)min?1、(1.23±1.23)min?1、(0.51±0.22)min?1,两组之间差异无统计学意义(P值均>0.05);良性结节:Max Slope、CER、IAUGC值分别为(0.10±0.10)s、(2.04±0.67)s、(0.78±0.38)s,恶性结节分别为(0.07±0.10)s、(1.55±0.67)s、(0.94±0.67)s,且两组间CER值差异有统计学差异(P<0.05),其对于区分良恶性结节的最佳特异度、敏感度分别为0.75、0.62,ROC曲线下面积AUC为0.72.TIC曲线显示甲状腺恶性结节多表现为Ⅱ型曲线,良性结节多表现为Ⅲ型曲线,且差异具有统计学意义(P=0.02).结论DCE-MRI中的半定量参数(CER)及TIC曲线类型对鉴别甲状腺良恶性结节有一定的诊断效能,初步数据显示基于DCE-MRI的定量参数在鉴别甲状腺良恶性结节方面价值有限,尚需要更大样本研究证实.

Background: This work aimed to explore the predictors of lymph node metastasis (LNM) and analyze the prognosis of patients with clinically node-negative (cN0) T1-T2 supraglottic laryngeal carcinoma (SGLC). Methods: Data for 130 patients with cN0 T1-T2 SGLC who initially underwent surgery were retrospectively reviewed. Occult LNM incidence, relevant factors, and prognosis were analyzed. Results: Of the 130 patients with cN0 T1-T2 SGLC, 21 (16.2%) had occult LNM. Based on univariate and multivariable regression analyses, male sex and poor tumor differentiation predicted the incidence of occult LNM. The incidence of occult LNM was 20.9% in males and 5.1% in females (p = 0.035). Patients with poorly differentiated tumors had a higher incidence of occult LNM (42.9%) than patients with well-differentiated (10.3%) and moderately differentiated tumors (14.3%; p < 0.05). Thirteen patients (10%) had cervical recurrence, and all had T2 tumors (p = 0.02). The 5-year disease-specific survival rates were 70 and 90% for patients with and without LNM, respectively (p = 0.000). Conclusions: Sex and tumor differentiation are potential predictors of occult nodal disease. Female patients with cN0 T1-T2 SGLC are less likely than male patients to have neck metastasis. Poorly differentiated tumors are associated with the frequency of neck metastasis, and selective neck dissection is strongly recommended for these tumors.