Diagnosis Of Lymph Node Metastasis Research Articles

Relationship between total lesion glycolysis of primary lesions based on 18F-FDG PET/CT and lymph node metastasis in gastric adenocarcinoma: a cross-sectional preliminary study.

We explored the relationship between lymph node metastasis (LNM) and total lesion glycolysis (TLG) of primary lesions determined by 18fluoro-2-deoxyglucose PET/computed tomography (18F-FDG PET/CT) in patients with gastric adenocarcinoma, and evaluated the independent effect of this association. This retrospective study included 106 gastric adenocarcinoma patients who were examined by preoperative 18F-FDG PET/CT imaging between April 2016 and April 2020. We measured TLG of primary gastric lesions and evaluated its association with LNM. Multivariate logistic regression and a two-piece-wise linear regression were performed to evaluate the relationship between TLG of primary lesions and LNM. Of the 106 patients, 75 cases (71%) had LNM and 31 cases (29%) did not have LNM. Univariate analyses revealed that a per-SD increase in TLG was independently associated with LNM [odds ratio (OR) = 2.37; 95% confidence interval (CI), 1.42-3.98; P = 0.0010]. After full adjustment of confounding factors, multivariate analyses exhibited that TLG of primary lesions was still significantly associated with LNM (OR per-SD: 2.20; 95% CI, 1.16-4.19; P = 0.0164). Generalized additive model indicated a nonlinear relationship and saturation effect between TLG of primary lesions and LNM. When TLG of primary lesions was <23.2, TLG was significantly correlated with LNM (OR = 1.26; 95% CI, 1.07-1.48; P = 0.0053), whereas when TLG of primary lesions was ≥ 23.2, the probability of LNM was greater than 60%, gradually reached saturation effect, as high as 80% or more. In this preliminary study, there were saturation and segmentation effects between TLG of primary lesions determined by preoperative 18F-FDG PET/CT and LNM. When TLG of primary lesions was ≥ 23.2, the probability of LNM was greater than 60%, gradually reached saturation effect, as high as 80% or more. TLG of primary lesions is helpful in the preoperative diagnosis of LNM in patients with gastric adenocarcinoma.

Thyroglobulin Measurement Through Fine-Needle Aspiration for Optimizing Neck Node Dissection in Papillary Thyroid Cancer

BackgroundThyroglobulin measurement in fine-needle aspiration (FNA-Tg) is an additional diagnostic tool of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC). However, its performance as a preoperative indicator of lateral neck LNM in PTC is unclear. We evaluated the use of FNA cytology and FNA-Tg to detect neck LNM presurgery using a simple methodology, and established a cut-off value for diagnosing LNM in PTC.MethodsWe performed a retrospective cohort study based on hospital records, including 299 FNA-Tg measurements from 228 patients with PTC. The cut-off value for FNA-Tg was obtained through a receiver operating characteristic (ROC) curve analysis. The relationships between various parameters and FNA-Tg were analyzed using Spearman’s correlation.ResultsOf 299 lymph nodes (LNs) from 228 patients following surgery, 151 were malignant and 148 were benign. The median FNA-Tg levels were 414.40 ng/mL and 6.36 ng/mL in the metastatic and benign LNs, respectively. An FNA-Tg cut-off value of 28.3 ng/mL had the best diagnostic performance (93.38% sensitivity, 70.27% specificity, area under the ROC curve [AUC] 0.868) in the whole cohort. The diagnostic value performed better in the lateral neck group (level II–V, n = 163) than in the central neck group (level VI, n = 136); in the lateral neck group, the sensitivity and specificity of the FNA-Tg cut-off (16.8 ng/mL) were 96.25% and 96.36%, respectively.ConclusionsFNA-Tg is a useful technique for the diagnosis of LNM before surgery, especially in lateral neck dissection.Clinical trial registration numberChiCTR1900028547.

The diagnostic value of chest CT and MRI for lymph node metastasis of thoracic esophageal cancer

Objective To compare the clinical value of chest CT and Magnetic Resonance Imaging (MRI) in the diagnosis of lymph node metastasis of thoracic esophageal cancer. Methods A retrospective analysis of 90 patients with thoracic esophageal cancer lymph node metastasis diagnosed and treated in our hospital from July 2015 to June 2019. All patients underwent chest CT and MRI scans after admission, and the lesion tissue samples were taken for pathological examination after surgery. Physical examination, with pathological diagnosis results as the gold standard, analyze the sensitivity and specificity of chest CT and MRI. Results The sensitivity, specificity, positive predictive value, and negative predictive value of MRI scan was 88.73%, 94.74%, 98.44%, and 69.23%, respectively, which were higher than 69.01%, 52.63%, 84.48%, and 31.25% of the chest CT scan. In the distribution of lymph node metastasis, the middle of the thorax was the largest, and the proportions of the upper, middle, and lower thoraxes were 26.67%, 60.00%, and 13.33%, respectively. The uppermost mediastinal and paratracheal lymph nodes had the largest metastases in the upper thorax, the paratracheal and subcarinal lymph nodes in the middle thoracic segment had the most metastasis, and the next to the cardia and left gastric artery lymph nodes in the lower thorax had the largest metastases. The accuracy of MRI diagnosis is higher than that of chest CT. Conclusion Both chest CT and MRI can diagnose lymph node metastasis of thoracic esophageal cancer. The diagnostic value of MRI in diagnosing lymph node metastasis of thoracic esophageal cancer is better than chest CT. The diagnostic accuracy of MRI in each part of the lymph node is higher than that of chest CT, which can show the lymph nodes more clearly in transfer situation. 摘要： 目的 比较胸部计算机断层扫描 (computer tomography, CT) 和磁共振成像 (magnetic resonance imaging, MRI) 判断胸段食管癌淋巴结转移的临床价值。 方法 回顾性分析2015年7月一2019年6月在我院诊治的胸段食管癌淋 巴结转移患者90例, 所有患者在入院后均采用胸部CT和MRI平扫及增强扫描, 并在手术后对病灶组织标本进行病 理学检测, 以病理诊断结果为金标准, 分析胸部CT和MRI灵敏度和特异度。 结果 MRI扫描, 灵敏度为88.73%, 特 异度为94.74%, 阳性预测值为98.44%, 阴性预测值为69.23%, 高于胸部CT扫描的69.01%、52.63%、84.48%、31.25%。淋巴结转移分布中, 胸中段最多, 胸上段、胸中段、胸下段所占比例分别为26.67%、60.00%、13.33%, 胸上段中 最上纵隔和气管旁淋巴结转移最多, 胸中段气管旁、隆突下淋巴结转移最多, 胸下段贲门旁、胃左动脉旁淋巴结转移最 多, MRI诊断准确率高于胸部CT。 结论 胸部CT、MRI均能够诊断出胸段食管癌淋巴结转移, MRI诊断胸段食管癌 淋巴结转移的诊断价值优于胸部CT, MRI在胸段淋巴结各部位的诊断准确率高于胸部CT, 能够更清晰显示淋巴结的 转移情况。

The diagnostic value of metabolic, morphological and heterogeneous parameters of 18F-FDG PET/CT in mediastinal lymph node metastasis of non-small cell lung cancer.

To investigate the value of PET/CT metabolic, morphological and heterogeneous parameters in the diagnosis of 18F-FDG positive mediastinal lymph node metastasis in non-small cell lung cancer (NSCLC). A total of 156 patients with pathologically diagnosed NSCLC and underwent 18F-FDG PET/CT scans were enrolled in this study. Mediastinal lymph nodes with 18F-FDG uptake greater than the mediastinum were analyzed. The metabolic parameters of maximum and mean standardized uptake value (SUVmax, SUVmean), SUVratio (node SUVmax/mediastinum SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), morphological parameters of maximum short diameter (Dmin), CT values and metabolic heterogeneity parameter of coefficient of variation (COV) were measured. The performance of each parameter and their combinations for diagnosis of lymph node metastasis was evaluated through receiver operating characteristic (ROC) curves and binary logistic regression analysis. There were 206 lymph nodes with pathological evidence included in the study, including 103 metastatic and 103 nonmetastatic nodes. The SUVmax, SUVmean, SUVratio, TLG, COV and Dmin of metastatic lymph nodes were significantly higher/greater than those in nonmetastatic ones (P < 0.05). ROC curve analysis revealed that the combination of SUVratio, Dmin and COV showed the highest diagnostic efficacy among all single and combined parameters, the area under the curve (AUC) was 0.907 (P = 0.000), these three parameters all increased the risk of lymph node metastasis, with odds ratios of 1.848, 1.293 and 1.258, respectively (all P < 0.05). Heterogeneity parameter was helpful for the accurate distinction of mediastinal lymph node metastasis in NSCLC. The combination of the SUVratio, Dmin and COV could improve the diagnostic accuracy. Multiple-parameters analysis plays an important complementary role in the diagnosis of lymph node metastasis.

Second-order multi-instance learning model for whole slide image classification

Whole slide histopathology images (WSIs) play a crucial role in diagnosing lymph node metastasis of breast cancer, which usually lack fine-grade annotations of tumor regions and have large resolutions (typically 105 × 105 pixels). Multi-instance learning has gradually become a dominant weakly supervised learning framework for WSI classification when only slide-level labels are available. In this paper, we develop a novel second-order multiple instances learning method (SoMIL) with an adaptive aggregator stacked by the attention mechanism and recurrent neural network (RNN) for histopathological image classification. To be specific, the proposed method applies a second-order pooling module (matrix power normalization covariance) for instance-level feature extraction of weakly supervised learning framework, attempting to explore second-order statistics of deep features for histopathological images. Additionally, we utilize an efficient channel attention mechanism to adaptively highlight the most discriminative instance features, followed by an RNN to update the final bag-level representation for the slide classification. Experimental results on the lymph node metastasis dataset of 2016 Camelyon grand challenge demonstrate the significant improvement of our proposed SoMIL framework compared with other state-of-the-art multi-instance learning methods. Moreover, in the external validation on 130 WSIs, SoMIL also achieves an impressive area under the curve performance that competitive to the fully-supervised framework.

Deep transfer learning based on magnetic resonance imaging can improve the diagnosis of lymph node metastasis in patients with rectal cancer.

Lymph node (LN) metastasis is the main prognostic factor for local recurrence and overall survival of patients with rectal cancer. The accurate evaluation of LN status in rectal cancer patients is associated with improved treatment and prognosis. This study aimed to apply deep transfer learning to classify LN status in patients with rectal cancer to improve N staging accuracy. The study included 129 patients with 325 rectal cancer screenshots of LN T2-weighted (T2W) images from April 2018 to March 2019. Deep learning was applied through a pre-trained model, Inception-v3, for recognition and detection of LN status. The results were compared to manual identification by experienced radiologists. Two radiologists reviewed images and independently identified their status using various criteria with or without short axial (SA) diameter measurements. The accuracy, positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC) were calculated. When the same radiologist performed the analysis, the AUC was not significantly different in the presence or absence of LN diameter measurements (P>0.05). In the deep transfer learning method, the PPV, NPV, sensitivity, and specificity were 95.2%, 95.3%, 95.3%, and 95.2%, respectively, and the AUC and accuracy were 0.994 and 95.7%, respectively. These results were all higher than that achieved with manual diagnosis by the radiologists. The internal details of LNs should be used as the main criteria for positive diagnosis when using MRI. Deep transfer learning can improve the MRI diagnosis of positive LN metastasis in patients with rectal cancer.

68 Ga-DOTATOC-PET/MRI and 11 C-5-HTP-PET/MRI are superior to 68 Ga-DOTATOC-PET/CT for neuroendocrine tumour imaging.

The present study aimed to assess gadoxetate disodium contrast-enhanced (CE) positron emission tomography (PET)/magnetic resonance imaging (MRI) with 68 Ga-DOTATOC and 11 C-5-Hydroxy-tryptophan (11 C-5-HTP) in comparison with iodine CE 68 Ga-DOTATOC-PET/computed tomography (CT) for neuroendocrine tumour imaging. Detection rate and reader's confidence were evaluated for each separate image volume: CE-CT, CE-MRI including diffusion-weighted imaging, 68 Ga-DOTATOC-PET performed at PET/CT, 68 Ga-DOTATOC-PET performed at PET/MRI and 11 C-5-HTP-PET, and for the three combined hybrid examinations 68 Ga-DOTATOC-PET/MRI, 11 C-5-HTP-PET/MRI and 68 Ga-DOTATOC-PET/CT. In 11 patients, 255 lesions were depicted. 68 Ga-DOTATOC-PET performed at PET/MRI depicted 72.5%, 68 Ga-DOTATOC-PET performed at PET/CT depicted 62.7%, 11 C-5-HTP-PET depicted 68.2% and CE-CT depicted 53% of lesions. 68 Ga-DOTATOC-PET performed at PET/MRI (P<0.001) and PET/CT (P=0.02), 11 C-5-HTP-PET (P<0.001) and MRI (P<0.001) were superior to CT. 68 Ga-DOTATOC-PET/MRI and 11 C-5-HTP-PET/MRI detected 92.5% and 92% of lesions, respectively, and both outperformed 68 Ga-DOTATOC-PET/CT (65%) (P<0.001). For liver metastasis imaging, MRI alone was unsurpassed (P<0.01) and 68 Ga-DOTATOC-PET/MRI and 11 C-5-HTP-PET/MRI outperformed 68 Ga-DOTATOC-PET/CT (P<0.001). For lymph node metastasis diagnosis, 68 Ga-DOTATOC-PET performed at PET/MRI and PET/CT and 11 C-5-HTP-PET detected 94%, 94% and 94% of lesions, respectively, and outperformed MRI and CE-CT alone (P<0.001). For bone metastasis imaging, 68 Ga-DOTATOC-PET performed at PET/MRI and PET/CT and 11 C-5-HTP-PET performed equally well (P=0.05) and better than MRI. Reader's confidence was better for 68 Ga-DOTATOC-PET/MRI and 11 C-5-HTP-PET/MRI than for 68 Ga-DOTATOC-PET/CT. The tumour maximum standardised uptake value and tumour-to-liver ratio were both approximately twice as high as for 68 Ga-DOTATOC than for 11 C-5-HTP. 68 Ga-DOTATOC-PET/MRI and 11 C-5-HTP-PET/MRI provided the highest detection rates and reader's confidence and were both superior to 68 Ga-DOTATOC-PET/CT, mainly because of the MRI component. The imaging contrast with 68 Ga-DOTATOC was superior to that of 11 C-5-HTP.

Ganglio centinela en la metástasis ganglionar por cáncer papilar de tiroides: validez diagnóstica y aplicación en la práctica clínica

IntroductionThe presence of lymph nodes metastasis in papillary thyroid cancer modifies the type of surgical resection as well as the indication of the treatment with 131I in the postoperative period. This therapeutic approach is based on the results of the diagnostic tests, like the cervical ultrasonography. Currently other methods of diagnostic are tested as selective sentinel lymph node biopsy (SLNB). It can complement to the ultrasound results. The aim was to validate the SLNB for use in the diagnosis of lymph node metastasis by papillary thyroid cancer. MethodsObservational prospective cohort study of 55 patients who underwent papillary thyroid cancer without suspicion of lymph node involvement clinical or radiological, since February 2012 through February 2015, with a follow-up between 6 and 8 years. It was used 99Tc with intratumoral nanocoloid and a portable tube of the gamma camera for the detection of the sentinel node. Variables: age, gender, histological, analytical and preoperative and postoperative staging. The sensitivity, specificity and predictive values of technique was calculated. The validation was determined by calculating the detectability and the false negative results of the test. ResultsIn 53 of the 55 patients (96.36%) there was the sentinel node detection. The false negative were 4 patients (7.5%). Of the rest, after applying the SLNB, 24 (48.9%) were kept as N0, 14 (28.5%) became N1a and 11 (22.4%) were classified as N1b. The differences observed in the study were significant (P<.05). The sensitivity was 86.21%, the specificity of 100%, the PPV was 100% and the NPV of 85.71%. The diagnostic accuracy was 92.45%. ConclusionsThe SLNB is a valid technique for use in patients suffering from papillary thyroid cancer with a high diagnostic accuracy.

The Utility of Ultrasonography in the Diagnosis of Cervical Lymph Nodes after Chemoradiotherapy for Head and Neck Squamous Cell Carcinoma.

Background and Objectives: There is evidence or consensus on the use of 18F-2-fluorodeoxyglucose-positron emission tomography with computed tomography (PET-CT) in evaluating the effects of treatment at 12 weeks after chemoradiotherapy for head and neck squamous cell carcinoma with cervical lymph node metastasis. However, the use of imaging to evaluate the effects of treatment within 12 weeks after chemoradiotherapy is controversial. The aim of this study was to evaluate the usefulness of ultrasonography in the diagnosis of lymph nodes metastasis after chemoradiotherapy according to the criteria of the “Evaluation of the effects of treatment on metastatic cervical lymph nodes using ultrasonography”, which evaluated lymph nodes metastasis based on size change and presence of degeneration. Materials and methods: This prospective study included 34 head and neck squamous cell carcinoma patients with cervical lymph nodes metastasis. Thirty-two patients who completed treatment were analyzed. Ultrasonography was performed at 4 and 8 weeks after chemoradiotherapy and we judged whether a favorable prognosis could be expected or whether additional treatments should be considered. Ultrasonography and PET-CT were performed at 12 weeks after chemoradiotherapy. Neck dissection was performed if residual disease was suspected based on the PET-CT findings. Results: The accuracy and negative predictive value of ultrasonography were 81.3% and 96.3%, respectively. According to the Ultrasonography findings, the size of lymph nodes metastasis after chemoradiotherapy was significantly smaller than those before chemoradiotherapy (p < 0.05). The fluid and blood flow of lymph nodes metastasis showed a significantly reduced at 12 weeks after chemoradiotherapy (p < 0.05, p < 0.05, respectively). The echo density significantly changed from low to high echoic density after chemoradiotherapy (p < 0.05). Conclusions: Ultrasonography was useful for evaluating cervical lymph nodes metastasis after chemoradiotherapy for head and neck squamous cell carcinoma.

Predictive Value of Folate Receptor-Positive Circulating Tumor Cells for the Preoperative Diagnosis of Lymph Node Metastasis in Patients with Lung Adenocarcinoma.

Noninvasive assessments of the risk of lymph node metastasis (LNM) in patients with lung adenocarcinoma (LAD) are of great value for selecting individualized treatment options. However, the diagnostic accuracies of different preoperative LN evaluation methods in routine clinical practice are not satisfactory. Here, an assessment to detect folate receptor (FR)-positive circulating tumor cells (CTCs) based on ligand-targeted enzyme-linked polymerization is established. FR-positive CTCs have the potential to improve the specificity and sensitivity of diagnosing LNM in lung cancer patients. The addition of CTC level improved the diagnostic efficiency of the initial prediction model that comprises other clinical parameters. A nomogram for predicting preoperative LNM is established, which showed good prediction and calibration capacities and achieved an average area under the curve of 0.786. Good correlations are observed between the CTC level and nodal classifications, such as the number of positive LNs and the ratio of the number of positive LNs to removed LNs (LN ratio or LNR). The ligand-targeted enzyme-linked polymerization-assisted assessment of CTCs enables noninvasive detection and has a useful predictive value for the preoperative diagnosis of LNM in patients with LAD.

Clinical Impact of Preoperative Magnetic Resonance Imaging in the Evaluation of Myometrial Infiltration and Lymph-Node Metastases in Stage I Endometrial Cancer.

: Purpose: In the developed world, endometrial cancer is one of the most common malignant gynecological cancer types. Due to the highly available diagnostic modalities and patient education, the early detection of the tumor leads to high overall survival. Methods: In this study we analyzed the reliability of preoperative MRI findings in the staging of early stage endometrial cancer, as well as the clinical characteristics of patients underwent radical hysterectomy and the histopathologic evaluation of their tumor, with the retrospective data of radical hysterectomies performed in our hospital between 2010 and 2019. Results: The accuracy, sensitivity, specificity, negative- and positive predictive value of MRI regarding stage were 94.7, 63.3, 94.8, 83.8, and 83.8%, respectively. The accuracy, sensitivity, specificity, negative- and positive predictive value of MRI for the detection of the myometrial invasion were 69.8, 80.0, 60.8, 64.3, and 77.5%, respectively. The accuracy, sensitivity, specificity, negative- and positive predictive value of MRI for the detection of lymph node metastases were 78.1, 28.6, 82, 11.1, and 93.6%, respectively. Conclusions: Based on our results, MRI is the method of choice in terms of evaluating overall staging, as well as myometrial invasion, as its specificity and negative predictive value are relatively high. However, systematic lymphadenectomy showed improved cancer-related survival and recurrence-free survival. Our studies showed that the diagnosis of lymph node metastases is difficult with MRI modality since hyperplastic and metastatic nodes cannot easily differentiate, leading to a high percentage of false-positive results. Therefore, other imaging modalities may be used for more accurate evaluation. New findings of our study were that the role of the radiologist’s expertise in the evaluation of MR imaging plays an essential role in lowering false-negative and false-positive results. Therefore, findings evaluated by a radiologist with high-level expertise in gynecological imaging can complement the clinical findings and help substantially define the needed treatment.

Prediction model for cervical lymph node metastasis in human papillomavirus-related oropharyngeal squamous cell carcinomas.

To develop and validate a risk scoring system based on clinical and imaging findings to predict lymph node metastasis from HPV-related oropharyngeal squamous cell carcinomas. This study population who had undergone neck dissections or lymph node biopsies in patients with HPV+ OPSCC was obtained from a historical cohort from two tertiary referral hospitals. The training set from one hospital included 455 lymph nodes from 82 patients, and the test set from the other hospital included 150 lymph nodes from 42 patients. The baseline clinical and imaging findings on pretreatment CT or MR were investigated and the reference standards were the histopathologic results. A risk scoring system was constructed based on logistic regression and validated both internally and externally. A 7-point risk scoring system was developed based on the following variables: central necrosis, infiltration of adjacent planes, lymph node level, and the maximal axial diameter of the lymph node. This risk scoring system showed good discriminative ability for metastasis in the training set (C-statistic 0.952; 95% CI, 0.931-0.972) and test set (C-statistic 0.968, 95% CI, 0.936-0.999) and good calibration ability in the training set (p = 0.723) and test set (p = 0.253). We developed and validated a reliable risk scoring system that predicts lymph node metastasis from HPV+ OPSCCs based on the clinical data and pretreatment imaging findings. We expect this risk scoring system to be a useful guide for better decision-making in practice. • It is important to diagnose lymph node metastasis from HPV+ OPSCC for treatment planning; however, there has been little research on that. • We developed and externally validated a new scoring system for stratifying the risk of lymph node metastasis from HPV+ OPSCC based on clinical and imaging data. • A predictive model combining both clinical and imaging data showed high diagnostic accuracy and efficiency for lymph node metastasis from HPV+ OPSCC.

Clinical biomarkers to predict preoperative lymph node metastasis in endometrial cancer

Background/Aim: Although the evaluation of preoperative lymph node metastasis is very important for the appropriate approach of the surgeon, it cannot be determined precisely. We aimed to investigate preoperative systemic inflammatory markers and the value of CA 125 in the prediction of lymph node metastasis (LNM) in epithelial endometrial carcinoma. Methods: In our retrospective cohort study, 327 patients were pathologically diagnosed with epithelial endometrial carcinoma and underwent surgical staging including lymphadenectomy. We investigated preoperative serum CA 125, neutrophil/lymphocyte (NLR) and thrombocyte/lymphocyte (PLR) values and their relationship with LNM. ROC analysis was performed to these variables for prediction of LNM. Results: There was a significant difference between histological type (P=0.021), myometrial invasion (P<0.001), lymphovascular space invasion (LVSI) (P<0.001), and peritoneal cytology (P=0.001) in those with and without LNM. Among the NLR, PLR and CA 125 values, only CA 125 was significantly higher in the LN positive group compared to the LN negative group (P=0.516, P=0.408 and P=0.001, respectively). The optimal CA 125 cut-off value in the preoperative diagnosis of LNM was ≥39.0 U/ml. The diagnostic sensitivity, specificity, PPV, NPV, and accuracy values of CA 125 were 77%, 82%, 48%, 94%, and 81%, respectively. Conclusion: While preoperative CA 125 value was a significant predictor for LNM in epithelial endometrial cancers, we did not detect this relationship in NLR, PLR and systemic inflammatory markers.

MRI associated biomarker analysis for diagnosis of lymph node metastasis in T1-2 stage rectal cancer

Objective: To explore the diagnostic accuracy improved by magnetic resonance imaging (MRI) biomarkers for lymph node metastasis in T1-2 stage rectal cancer before treatment. Methods: Medical records of 327 patients with T1-2 rectal cancer who underwent pretreatment MRI and rectal tumor resection between January 2015 and November 2019 were retrospectively analyzed. Fifty-seven cases were divided into the lymph node metastasis group (N+ group) while other 270 cases in the non-lymph node metastasis group (N-group) according to the pathologic diagnosis. Two radiologist evaluated the tumor characteristics of MRI images. The relationship of the clinical and imaging characteristics of lymph node metastasis was assessed by using univariate analysis and multivariable logistic regression analysis. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic abilities for the differentiation of N- from N+ tumors. Results: Among the 327 patients, MR-N evaluation was positive in 67 cases, which was statistically different from the pathological diagnosis (P<0.001). The sensitivity, specificity and accuracy of MRI for lymph node metastasis were 45.6%, 84.8% and 78.0%, respectively. Multivariate regression analysis showed that tumor morphology (P=0.002), including mucus or not (P<0.001), and MR-N evaluation (P<0.001) were independent influencing factors for stage T1-2 rectal cancer with lymph node metastasis. The area under the ROC curve of rectal cancer with lymph node metastasis analyzed by the logistic regression model was 0.786 (95%CI: 0.720~0.852). Conclusions: Tumor morphology, including mucus or not, and MR-N evaluation can serve as independent biomarkers for differentiation of N- and N+ tumors. The model combined with these biomarkers facilitates to improve the diagnostic accuracy of lymph node metastasis in T1-2 rectal cancers by using MRI.

Magnetic resonance imaging (MRI) versus computed tomography (CT) for the diagnosis of lymph node metastasis in preoperative rectal cancer

Magnetic resonance imaging (MRI) versus computed tomography (CT) for the diagnosis of lymph node metastasis in preoperative rectal cancer

Abstract PS1-39: Evaluation of novel diagnostic kits using the semi-dry dot-blot method combined with an automatic reader for detecting metastases in lymph nodes of patients with breast cancer : A single-center prospective study

Abstract Background: The semi-dry dot-blot (SDB) method, a diagnostic procedure for detecting lymph node (LN) metastases using anti-cytokeratin (CK) antibody, is based on the theory that epithelial components such as CK are not found in normal LNs. Thus, metastases are diagnosed on basis of the presence of CK in lavage fluid of sectioned LNs. We prospectively evaluated novel SDB kits that use a newly developed anti-CK19 antibody and an automatic reader for diagnosing LN metastases in patients with breast cancer. Methods: We obtained 117 LNs dissected from 58 patients with breast cancer between January 2020 and June 2020 at Nagasaki University Hospital. These were sliced at 2-mm intervals and washed with phosphate-buffered saline. Cells suspended in the lavage fluid of sliced LNs were centrifuged and lysed to extract protein. The extracted protein was applied to the SDB kit to diagnose LN metastasis using an automatic reader that evaluates absorbance. Hematoxylin and eosin (H&amp;E) stained washed LNs were blindly examined by pathologists. Diagnoses based on SDB kit and automatic reader findings were compared with diagnoses made by histological examination of paraffin-embedded H&amp;E stained sections of the LNs. Results: Six of the 117 LNs were assessed as positive and 111 as negative by histological examination. With a borderline CK19 absorbance of 50 milli-absorbance (mAbs) for detecting LN metastases excluding isolated tumor cells, the sensitivity, specificity, and overall agreement of the SDB kit were 66.7%, 99.1%, and 97.4%, respectively. Two patients with false-negatives had micrometastases of 0.6 and 0.5 mm in diameter. There was contamination by breast epithelial tissue in one false-positive case. With a borderline CK19 absorbance of 100 mAbs for distinguishing macrometastases from micrometastases, the sensitivity, specificity, and overall agreement of the SDB kit were 100%, 100%, and 100%, respectively. Furthermore, the kits and the automatic reader yielded diagnoses in approximately 20 min at a cost of less than 30 USD. Conclusions: The kits with an automatic reader used in our study were accurate, quick, and cost-effective in diagnosing LN metastases without loss of LN tissue, and were especially useful for detecting distinguish macrometastases. We plan to start a prospective multi-center study to evaluate clinical performance soon. Citation Format: Ryota Otsubo, Hiroshi Yano, Megumi Matsumoto, Aya Tanaka, Ayako Fukushima, Sayaka Kuba, Michi Morita, Naoki Uno, Han-Seung Yoon, Katsunori Yanagihara, Susumu Eguchi, Takeshi Nagayasu. Evaluation of novel diagnostic kits using the semi-dry dot-blot method combined with an automatic reader for detecting metastases in lymph nodes of patients with breast cancer : A single-center prospective study [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS1-39.

Feasibility of T2WI-MRI-based radiomics nomogram for predicting normal-sized pelvic lymph node metastasis in cervical cancer patients.

To investigate the feasibility of T2WI-based radiomics nomogram analysis to non-invasively predict normal-sized pelvic lymph node (LN) metastasis (LNM) in cervical cancer patients. Preoperative images of 219 normal-sized pathologically confirmed LNs from 132 cervical cancer patients admitted to our hospital between January 2013 and March 2020 were retrospectively reviewed. Regions of interests (ROIs) were separately delineated on whole LNs and tumors. The maximum-relevance and minimum-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods were used for the construction of radiomics signature. Logistic regression modeling was employed to build models based on clinical features on LN T2WI (model 1), model 1 combined with LN radiomics features (model 2), and model 2 combined with tumor score (model 3). Diagnostic performance was assessed and compared. Both model 2 and model 3 showed higher diagnostic accuracy (training: model 2 0.75, model 3 0.78, model 1 0.72; validation: model 2 0.77, model 3 0.69, model 1 0.66) and AUC (training: model 2 0.77, model 3 0.82, model 1 0.74; validation: model 2 0.75, model 3 0.74, model 1 0.70) than clinical model 1. Diagnostic performance of model 3 was improved compared with model 2 in primary cohort, but reduced in validation cohort. However, the differences did not show obvious statistical difference (p = 0.05 and p = 0.15). T2WI-based radiomics nomogram incorporating the LN radiomics signature with the clinical morphological LN features is promising for predicting the normal-sized pelvic LNM in cervical cancer patients. The original tumor radiomics analysis did not significantly improve the differential diagnosis of LNM. • The combination of LN radiomics signature with LN clinical morphological features on T2WI could discriminate LNM relatively well. • The tumor radiomics analysis did not significantly improve the differential diagnosis of LNM.

CT radiomics features to predict lymph node metastasis in advanced esophageal squamous cell carcinoma and to discriminate between regional and non-regional lymph node metastasis: a case control study.

Prediction of lymph node status in esophageal squamous cell carcinoma (ESCC) is critical for clinical decision making. In clinical practice, computed tomography (CT) has been frequently used to assist in the preoperative staging of ESCC. Texture analysis can provide more information to reflect potential biological heterogeneity based on CT. A nomogram for the preoperative diagnosis of lymph node metastasis in patients with resectable ESCC has been previously developed. However, to the best of our knowledge, no reports focus on developing CT radiomics features to discriminate ESCC patients with regional lymph node metastasis (RLNM) and non-regional lymph node metastasis (NRLNM). We, therefore, aimed to develop CT radiomics models to predict lymph node metastasis (LNM) in advanced ESCC and to discriminate ESCC between RLNM and NRLNM. This study enrolled 334 patients with pathologically confirmed advanced ESCC, including 152 patients without LNM and 182 patients with LNM, and 103 patients with RLNM and 79 patients NRLNM. Radiomics features were extracted from CT data for each patient. The least absolute shrinkage and selection operator (LASSO) model and independent samples t-tests or Mann-Whitney U tests were exploited for dimension reduction and selection of radiomics features. Optimal radiomics features were chosen using multivariable logistic regression analysis. The discriminating performance was assessed by area under the receiver operating characteristic curve (AUC) and accuracy. The radiomics features were developed based on multivariable logistic regression and were significantly associated with LNM status in both the training and validation cohorts (P<0.001). The radiomics models could differentiate between patients with and without LNM (AUC =0.79 and 0.75, and accuracy =0.75 and 0.71 in the training and validation cohorts, respectively). In patients with LNM, the radiomics features could effectively differentiate between RLNM and NRLNM (AUC =0.98 and 0.95, and accuracy =0.94 and 0.83 in the training and validation cohorts, respectively). CT radiomics features could help predict the LNM status of advanced ESCC patients and effectively discriminate ESCC between RLNM and NRLNM.

Head-to-Head Comparison of 68Ga-Prostate-Specific Membrane Antigen PET/CT and Ferumoxtran-10-Enhanced MRI for the Diagnosis of Lymph Node Metastases in Prostate Cancer Patients.

Accurate assessment of lymph node (LN) metastases in prostate cancer (PCa) patients is critical for prognosis and patient management. Both prostate-specific membrane antigen (PSMA) PET/CT and ferumoxtran-10 nanoparticle-enhanced MRI (nano-MRI) are imaging modalities with high potential to identify LN metastases in PCa patients. The aim of this study was to compare the results of these imaging technologies in terms of characteristics and anatomic localization of suspicious LNs in order to assess the feasibility of their complementary use for imaging in PCa patients. Methods: In total, 45 patients with either primary PCa (n = 8) or recurrence (n = 36) were included in this retrospective study. All patients underwent both 68Ga-PSMA PET/CT and nano-MRI between October 2015 and July 2017 within 3 wk. Both scans were performed at the same institution according to local clinical protocols. All scans were analyzed independently by experienced nuclear medicine physicians and radiologists. The size, anatomic location, and level of suspicion were determined for all visible LNs. Subsequently, the findings from 68Ga-PSMA PET/CT and nano-MRI were compared without respect to a reference standard. Results: In total, 179 suspicious LNs were identified. Significantly more suspicious LNs per patient were detected by nano-MRI (P < 0.001): 160 were identified in 33 patients by nano-MRI, versus 71 in 25 patients by 68Ga-PSMA PET/CT. Of all suspicious LNs, 108 were identified only by nano-MRI (60%), 19 (11%) only by 68Ga-PSMA PET/CT, and 52 (29%) by both methods. The mean size of the suspicious LNs as identified by nano-MRI was significantly smaller (5.3 mm) than that by 68Ga-PSMA PET/CT (6.0 mm; P = 0.006). The median level of suspicion did not differ significantly. Both modalities identified suspicious LNs in all anatomic regions of the pelvis. Conclusion: Both modalities identified suspicious LNs that were missed by the other. Both modalities identified suspicious LNs in all anatomic regions of the pelvis; however, nano-MRI appeared to be superior in detecting smaller suspicious LNs. These findings suggest that nano-MRI has a potential role as a complement to PSMA PET/CT. However, since the clinical implications of the different results are not well established yet, further investigation of this complementary use is encouraged.

Artificial intelligence for the diagnosis of lymph node metastases in patients with abdominopelvic malignancy: a systematic review and meta-analysis.

Background: Accurate clinical diagnosis of lymph node metastases is of paramount importance in the treatment of patients with abdominopelvic malignancy. This review assesses the diagnostic performance of deep learning algorithms and radiomics models for lymph node metastases in abdominopelvic malignancies.