Lymph Node Metastasis In Patients Research Articles

Risk factors and prognosis for supraclavicular lymph node metastasis in patients with thoracic esophageal cancer. Distant or regional metastasis?

We aimed to determine the frequency and prognosis of supraclavicular (#104) lymph node (LN) metastasis compared with other LN stations in patients with advanced thoracic esophageal cancer and to identify risk factors for metastasis to delineate the indications for three-field lymphadenectomy (3FL). The study cohort of 567 eligible patients with esophageal cancer had undergone subtotal esophagectomy from 2003 to 2020. LN metastasis was defined as pathologically proven metastasis or positron emission tomography-positive LNs. The efficacy index (EI), calculated from the frequency of LN metastases and survival rates, was used as prognostic value of each LN station dissection for patient survival. Risk factors for #104 LN metastasis were determined by multivariable logistic regression. The frequency of #104 LN metastasis was 11.6% overall, 31.7% in upper and 8.3% in middle/lower third lesion. Neoadjuvant chemotherapy was administered to 71% of patients and chemo-radiation to 11%. The 5-year overall survival was 45.8%. The EI for #104 LNs (5.3) was similar to that for #101 LNs. Risk factors were age < 65years, upper third lesion, clinical N2-3, #101/106rec LN metastasis and poorly differentiated carcinoma. The 5-year overall survival of patients with middle/lower lesions was 38% (EI 3.1), similar to that for #101 and #8/9/11 LNs. The prognosis of patients with #104 LN metastases is similar to that of patients with metastases in other regional LN stations. Therefore, we recommend 3FL exclusively for patients at a high risk of #104 LN metastasis due to the overall metastatic rate not being high.

The Depth of Invasion and Level IV Cervical Node Metastasis in Patients with Clinically N0 Tongue Cancer.

The accurate indication for level IV dissection is crucial for preventing complications such as phrenic nerve damage and chylous fistulas in clinically N0 tongue cancer. Although the depth of invasion is an established independent risk factor for occult lymph node metastasis in tongue cancer, its relationship with level IV metastasis has not been evaluated. This study investigated the relationship between the depth of invasion and level IV nodal metastasis in clinically N0 tongue cancer. We retrospectively investigated clinical N0 patients who underwent glossectomy and level I-IV neck dissection. We examined lymph node metastasis, risk factors, and the relationship between depth of invasion and metastasis. Our study included 58 patients, and no patient had isolated level IV metastasis. Additionally, there was no level IV metastasis in well-differentiated tumors. Tumor size, depth of invasion, differentiation, and perineural invasion were significantly associated with level IV neck metastasis. We found a critical tumor size of 2.5cm and depth of invasion of 8mm for level IV neck metastasis. Based on our findings, we recommend that level IV dissection should be considered for poorly differentiated tumors, tumors greater than 2.5cm in size, and those deeper than 8mm. This study highlights the importance of depth of invasion as a prognostic factor for predicting level IV metastasis and suggests that our findings can be used to prevent unnecessary level IV dissections that may lead to complications in tongue cancer surgery.

Head-to-head comparison of [68Ga]Ga-DOTA-FAPI-04 and [18F]FDG PET/CT for the evaluation of tonsil cancer and lymph node metastases: a single-centre retrospective study

BackgroundThis study aimed to compare the diagnostic value of [68 Ga]Ga-DOTA-FAPI-04 and [18F]FDG PET/CT imaging for primary lesions and metastatic lymph nodes in patients with tonsil cancer.MethodTwenty-one tonsil cancer patients who underwent [68 Ga]Ga-DOTA-FAPI-04 and [18F]FDG PET/CT scans within two weeks in our centre were retrospectively enrolled. The maximum standardized uptake value (SUVmax) and tumor-to-background ratio (TBR) of the two tracers were compared by using the Mann‒Whitney U test. In addition, the sensitivity, specificity, and accuracy of the two methods for diagnosing metastatic lymph nodes were analysed.ResultsIn detecting primary lesions, the efficiency was higher for [68 Ga]Ga-DOTA-FAPI-04 PET/CT (20/22) than for [18F]FDG PET/CT (9/22). Although [68 Ga]Ga-DOTA-FAPI-04 uptake (SUVmax, 5.03 ± 4.06) was lower than [18F]FDG uptake (SUVmax, 7.90 ± 4.84, P = 0.006), [68 Ga]Ga-DOTA-FAPI-04 improved the distinction between the primary tumor and contralateral normal tonsillar tissue. The TBR was significantly higher for [68 Ga]Ga-DOTA-FAPI-04 PET/CT (3.19 ± 2.06) than for [18F]FDG PET/CT (1.89 ± 1.80) (p < 0.001). In lymph node analysis, SUVmax and TBR were not significantly different between [68 Ga]Ga-DOTA-FAPI-04 and [18F]FDG PET/CT (7.67 ± 5.88 vs. 8.36 ± 6.15, P = 0.498 and 5.56 ± 4.02 vs. 4.26 ± 3.16, P = 0.123, respectively). The specificity and accuracy of [68 Ga]Ga-DOTA-FAPI-04 PET/CT were higher than those of [18F]FDG PET/CT in diagnosing metastatic cervical lymph nodes (all P < 0.05).ConclusionThe availability of [68 Ga]Ga-DOTA-FAPI-04 complements the diagnostic results of [18F]FDG by improving the detection rate of primary lesions and the diagnostic accuracy of cervical metastatic lymph nodes in tonsil cancer compared to [18F]FDG.

Preoperative assessment of lymph nodal metastases with [68Ga]Ga-DOTATOC PET radiomics for improved surgical planning in well-differentiated pancreatic neuroendocrine tumours.

Accurate identification of lymph node (LN) metastases is pivotal for surgical planning of pancreatic neuroendocrine tumours (PanNETs); however, current imaging techniques have sub-optimal diagnostic sensitivity. Aim of this study is to investigate whether [68Ga]Ga-DOTATOC PET radiomics might improve the identification of LN metastases in patients with non-functioning PanNET (NF-PanNET) referred to surgical intervention. Seventy-two patients who performed preoperative [68Ga]Ga-DOTATOC PET between December 2017 and March 2022 for NF-PanNET. [68Ga]Ga-DOTATOC PET qualitative assessment of LN metastases was measured using diagnostic balanced accuracy (bACC), sensitivity (SN), specificity (SP), positive and negative predictive values (PPV, NPV). SUVmax, SUVmean, Somatostatin receptor density (SRD), total lesion SRD (TLSRD) and IBSI-compliant radiomic features (RFs) were obtained from the primary tumours. To predict LN involvement, these parameters were engineered, selected and used to train different machine learning models. Models were validated using tenfold repeated cross-validation and control models were developed. Models' bACC, SN, SP, PPV and NPV were collected and compared (Kruskal-Wallis, Mann-Whitney). LN metastases were detected in 29/72 patients at histology. [68Ga]Ga-DOTATOC PET qualitative examination of LN involvement provided bACC = 60%, SN = 24%, SP = 95%, PPV = 78% and NPV = 65%. The best-performing radiomic model provided a bACC = 70%, SN = 77%, SP = 61%, PPV = 60% and NPV = 83% (outperforming the control model, p < 0.05*). In this study, [68Ga]Ga-DOTATOC PET radiomics allowed to increase diagnostic sensitivity in detecting LN metastases from 24 to 77% in NF-PanNET patients candidate to surgery. Especially in case of micrometastatic involvement, this approach might assist clinicians in a better patients' stratification.

Ultrasound-based radiomics machine learning models for diagnosing cervical lymph node metastasis in patients with non-small cell lung cancer: a multicentre study

BackgroundCervical lymph node metastasis (LNM) is an important prognostic factor for patients with non-small cell lung cancer (NSCLC). We aimed to develop and validate machine learning models that use ultrasound radiomic and descriptive semantic features to diagnose cervical LNM in patients with NSCLC.MethodsThis study included NSCLC patients who underwent neck ultrasound examination followed by cervical lymph node (LN) biopsy between January 2019 and January 2022 from three institutes. Radiomic features were extracted from the ultrasound images at the maximum cross-sectional areas of cervical LNs. Logistic regression (LR) and random forest (RF) models were developed. Model performance was assessed by the area under the curve (AUC) and accuracy, validated internally and externally by fivefold cross-validation and hold-out method, respectively.ResultsIn total, 313 patients with a median age of 64 years were included, and 276 (88.18%) had cervical LNM. Three descriptive semantic features, including long diameter, shape, and corticomedullary boundary, were selected by multivariate analysis. Out of the 474 identified radiomic features, 9 were determined to fit the LR model, while 15 fit the RF model. The average AUCs of the semantic and radiomics models were 0.876 (range: 0.781–0.961) and 0.883 (range: 0.798–0.966), respectively. However, the average AUC was higher for the semantic-radiomics combined LR model (0.901; range: 0.862–0.927). When the RF algorithm was applied, the average AUCs of the radiomics and semantic-radiomics combined models were improved to 0.908 (range: 0.837–0.966) and 0.922 (range: 0.872–0.982), respectively. The models tested by the hold-out method had similar results, with the semantic-radiomics combined RF model achieving the highest AUC value of 0.901 (95% CI, 0.886–0.968).ConclusionsThe ultrasound radiomic models showed potential for accurately diagnosing cervical LNM in patients with NSCLC when integrated with descriptive semantic features. The RF model outperformed the conventional LR model in diagnosing cervical LNM in NSCLC patients.

Ultrasound and clinical factors predicting central lymph node metastases in patients with unilateral multifocal papillary thyroid carcinoma.

This retrospective study involving a large dataset of unilateral multifocal papillary thyroid carcinoma (UM-PTC) sought to identify factors that predict central lymph node metastases (CLNM) in patients. We identified a cohort of 158 patients who underwent cervical ultrasonography followed by UM-PTC diagnosis based on postoperative pathology. The relationship between CLNM and UM-PTC clinical ultrasound features was evaluated using univariate and multivariate analyses. Receiver operating characteristic (ROC) curve analysis was used to determine the ability of total tumor diameter (TTD) to predict CLNM. Among the 158 UM-PTC patients, the incidence of CLNM was 29.7% (47/158). Univariate and multivariate analyses revealed that a number of similarity of sonographic features (NSSF) ≥4 (odds ratio [OR]=11.335, 95% confidence interval [CI]: 3.95-32.50, p=0.000), microcalcifications (OR=3.54, 95% CI: 1.30-9.70, p=0.014), a TTD of ≥2cm (OR=4.48, 95% CI: 1.62-12.34, p=0.004), number of nodules ≥3 (OR=13.17, 95% CI: 3.24-53.52, p=0.000), and Lateral cervical lymph node metastasis (LLNM) (OR=5.57, 95% CI: 1.59-19.48, p=0.007) were independently associated with CLNM in UM-PTC. ROC curve analysis revealed that the TTD cut-off of 1.795cm had a sensitivity of 0.723 and a specificity of 0.676 for predicting CLNM. Patients with UM-PTC are at high risk of CLNM. NSSF ≥4, microcalcifications, TTD of ≥2cm, LLNM, and a number of nodules ≥3 were independently associated with CLNM. Our data show that ultrasound may guide surgical decisions in the treatment of UM-PTC.

Expression of nuclear receptor co‑activator 7 protein is associated with poor prognosis of breast cancer.

Nuclear receptor coactivator 7 (NCOA7) is an estrogen receptor binding protein. Its role in breast cancer progression has so far remained elusive. The present study aimed to determine the expression levels of NCOA7 in breast tumor samples and confirmed its potential utility as a breast cancer prognostic biomarker. The expression of NCOA7 was detected by immunohistochemical staining in 241 breast cancer tumor samples and 163 adjacent normal tissue samples. The association of NCOA7 expression with the clinicopathological characteristics and overall survival were statistically analyzed. Cell proliferation was determined by Cell Counting Kit-8 and colony-formation assays. Cell migration was detected using wound-healing and Transwell assays. NCOA7 was positively expressed in 44% of breast tumor tissues. The expression of NCOA7 was positively associated with tumor size (T-stage; P=0.005) and lymph node metastasis (N-stage; P=0.008). Additional statistical analysis indicated that the expression of NCOA7 was associated with patient age, tumor size and lymph node metastasis in patients with triple-negative breast cancer (TNBC) compared with that in patients with non-TNBC. The overall survival of patients with NCOA7-positive breast cancer was significantly lower than that of patients with NCOA7-negative breast cancer (P=0.006). Among the patients with lymph node metastasis, the overall survival was reversely associated with the expression of NCOA7 (P=0.042). Furthermore, knockdown of NCOA7 expression in breast cancer T47D and MCF7 cells significantly inhibited both cell proliferation and migration, suggesting that this protein may exert a role in driving breast cancer progression. Taken together, these results indicate that the expression of NCOA7 is associated with poor prognosis of breast cancer and suggest that this protein may be a driver for metastasis and a potential therapeutic target for advanced breast cancer.

Interpretable machine learning-based clinical prediction model for predicting lymph node metastasis in patients with intrahepatic cholangiocarcinoma

ObjectivePrediction of lymph node metastasis (LNM) for intrahepatic cholangiocarcinoma (ICC) is critical for the treatment regimen and prognosis. We aim to develop and validate machine learning (ML)-based predictive models for LNM in patients with ICC.MethodsA total of 345 patients with clinicopathological characteristics confirmed ICC from Jan 2007 to Jan 2019 were enrolled. The predictors of LNM were identified by the least absolute shrinkage and selection operator (LASSO) and logistic analysis. The selected variables were used for developing prediction models for LNM by six ML algorithms, including Logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision tree (DT), Multilayer perceptron (MLP). We applied 10-fold cross validation as internal validation and calculated the average of the areas under the receiver operating characteristic (ROC) curve to measure the performance of all models. A feature selection approach was applied to identify importance of predictors in each model. The heat map was used to investigate the correlation of features. Finally, we established a web calculator using the best-performing model.ResultsIn multivariate logistic regression analysis, factors including alcoholic liver disease (ALD), smoking, boundary, diameter, and white blood cell (WBC) were identified as independent predictors for LNM in patients with ICC. In internal validation, the average values of AUC of six models ranged from 0.820 to 0.908. The XGB model was identified as the best model, the average AUC was 0.908. Finally, we established a web calculator by XGB model, which was useful for clinicians to calculate the likelihood of LNM.ConclusionThe proposed ML-based predicted models had a good performance to predict LNM of patients with ICC. XGB performed best. A web calculator based on the ML algorithm showed promise in assisting clinicians to predict LNM and developed individualized medical plans.

Correlation between sentinel lymph node biopsy and non-sentinel lymph node metastasis in patients with cN0 breast carcinoma: comparison of invasive ductal carcinoma and invasive lobular carcinoma

BackgroundSome studies have suggested that axillary lymph node dissection (ALND) can be avoided in women with cN0 breast cancer with 1–2 positive sentinel nodes (SLNs). However, these studies included only a few patients with invasive lobular carcinoma (ILC), so the validity of omitting ALDN in these patients remains controversial. This study compared the frequency of non-sentinel lymph nodes (non-SLNs) metastases in ILC and invasive ductal carcinoma (IDC). Materials Methods: Data relating to a total of 2583 patients with infiltrating breast carcinoma operated at our institution between 2012 and 2023 were retrospectively analyzed: 2242 (86.8%) with IDC and 341 (13.2%) with ILC. We compared the incidence of metastasis to SLNs and non-SLNs between the ILC and IDC cohorts and examined factors that influenced non-SLNs metastasis. Results: SLN biopsies were performed in 315 patients with ILC and 2018 patients with IDC. Metastases to the SLNs were found in 78/315 (24.8%) patients with ILC and in 460 (22.8%) patients with IDC (p = 0.31). The incidence of metastases to non-SLNs was significantly higher (p = 0.02) in ILC (52/78–66.7%) compared to IDC (207/460 − 45%). Multivariate analysis showed that ILC was the most influential predictive factor in predicting the presence of metastasis to non-SLNs. Conclusions: ILC cases have more non-SLNs metastases than IDC cases in SLN-positive patients. The ILC is essential for predicting non-SLN positivity in macro-metastases in the SLN. The option of omitting ALND in patients with ILC with 1–2 positive SLNs still requires further investigation.

Prediction of lymph node metastasis in patients with papillary thyroid cancer based on radiomics analysis and intraoperative frozen section analysis: A retrospective study.

To evaluate the diagnostic efficiency among the clinical model, the radiomics model and the nomogram that combined radiomics features, frozen section (FS) analysis and clinical characteristics for the prediction of lymph node (LN) metastasis in patients with papillary thyroid cancer (PTC). A total of 208 patients were randomly divided into two groups randomly with a proportion of 7:3 for the training groups (n = 146) and the validation groups (n = 62). The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for the selection of radiomics features extracted from ultrasound (US) images. Univariate and multivariate logistic analyses were used to select predictors associated with the status of LN. The clinical model, radiomics model and nomogram were subsequently established by logistic regression machine learning. The area under the curve (AUC), sensitivity and specificity were used to evaluate the diagnostic performance of the different models. The Delong test was used to compare the AUC of the three models. Multivariate analysis indicated that age, size group, Adler grade, ACR score and the psammoma body group were independent predictors of lymph node metastasis (LNM). The results showed that in both the training and validation groups, the nomogram showed better performance than the clinical model, albeit not statistically significant (p > .05), and significantly outperformed the radiomics model (p < .05). However, the nomogram exhibits a slight improvement in sensitivity that could reduce the incidence of false negatives. We propose that the nomogram holds substantial promise as an effective tool for predicting LNM in patients with PTC.

TERT RNAscope analysis of sub-centimetric papillary thyroid carcinomas and synchronous lymph node metastases.

Sub-centrimetric papillary thyroid carcinomas usually have a good prognosis with a cancer specific survival of > 99%, however in up to 65% of patients, lymph node metastases can be observed. Molecular alterations in BRAF, TERT and TP53 are associated with worse clinicopathological outcome in patients with papillary thyroid carcinoma. Twenty-two cases of papillary thyroid carcinomas measuring ≤ 1cm with synchronous lymph node metastases were examined regarding morphological patterns and immunohistochemical status of p53, Ki-67, and BRAF V600E status. TERT RNA expression in lymph node metastases were evaluated by RNAScope®. Morphological patterns were heterogeneous in both primary tumors and lymph node metastases. Proliferation indices measured by Ki-67 were low. Both primary and lymph node metastases were wild type for p53 by immunohistochemical analysis. No lymph node metastasis showed TERT expression by RNAScope®. Our data indicate that TERT expression is not involved in the development early lymph node metastasis in patients with sub-centimetric PTC.

Low-Level Expression of p-S6 Is Associated with Nodal Metastasis in Patients with Head and Neck Cutaneous Squamous Cell Carcinoma.

Cutaneous squamous cell carcinoma (cSCC) is the second most common form of skin cancer. The incidence of metastasis for cSCC is estimated to be around 1.2-5%. Ribosomal protein S6 (p-S6) and the p21 protein (p21) are two proteins that play central roles in other cancers. These proteins may be equally important in cSCC, and together, these could constitute a good candidate for metastasis risk assessment of these patients. We investigate the relationship of p-S6 and p21 expression with the impact on the prognosis of head and neck cSCC (cSCCHN). p-S6 and p21 expression was analyzed by immunohistochemistry on paraffin-embedded tissue samples from 116 patients with cSCCHN and associations sought with clinical characteristics. Kaplan-Meier estimators and Cox proportional hazard regression models were also used. The expression of p-S6 was significantly inversely associated with tumor thickness, tumor size, desmoplastic growth, pathological stage, perineural invasion and tumor buds. p21 expression was significantly inversely correlated with >6 mm tumor thickness, desmoplastic growth, and perineural invasion. p-S6-negative expression significantly predicted an increased risk of nodal metastasis (HR = 2.63, 95% CI 1.51-4.54; p < 0.001). p21 expression was not found to be a significant risk factor for nodal metastasis. These findings demonstrate that p-S6-negative expression is an independent predictor of nodal metastasis. The immunohistochemical expression of p-S6 might aid in better risk stratification and management of patients with cSCCHN.

Prognostic significance of a pathological response in metastatic lymph nodes of patients with gastric cancer who underwent neoadjuvant chemotherapy followed by surgery.

To grade the pathological response of lymph nodes (LNs) to neoadjuvant chemotherapy (NAC) in patients with locally advanced gastric cancer (LAGC) and investigate its prognostic significance. This retrospective study included 196 patients who underwent NAC, followed by radical gastrectomy for LAGC between January 2010 and October 2019. Pathological responses were evaluated based on the proportion of residual tumor cells within the tumor area in the primary tumor (PT) and LNs and included the following categories: 1a (0%), 1b (< 10%), 2 (10-50%), and 3 (> 50%). Among 166 patients with clinically node-positive disease, 38/27/39/62 were classified as having LN regression grade (LRG) 1a/1b/2/3, respectively. Compared to LN non-responders (LRG 2 or 3), LN responders (LRG 1a or 1b) had significantly higher 5-year overall survival (72.5% vs. 19.0%, P < 0.001) and recurrence-free survival rates (67.8% vs. 22.2%, P < 0.001), irrespective of PT response. Furthermore, a multivariate analysis revealed that the LN response was an independent risk factor for the overall survival (hazard ratio [HR] 0.417, 95% confidence interval [CI] 0.181-0.962, P = 0.040) and recurrence-free survival (HR 0.490, 95% CI 0.242-0.991, P = 0.047), but not the PT response (P > 0.05). The pathological LN response may be a reliable prognostic prediction tool in patients with LAGC who received NAC.

Development and validation of a nomogram for predicting lymph node metastasis in ductal carcinoma in situ with microinvasion: A SEER population-based study.

Ductal carcinoma in situ with microinvasion (DCIS-MI) is a special type of breast cancer. It is an invasive lesion less than 1.0 mm in size related to simple ductal carcinoma in situ (DCIS). Lymph node metastasis (LNM) in DCIS-MI often indicates a poor prognosis. Therefore, the management of lymph nodes plays a vital role in the treatment strategy of DCIS-MI. Since DCIS-MI is often diagnosed by postoperative paraffin section and immunohistochemical detection, to obtain the best clinical benefits for such patients, we aim to establish and verify a nomogram to predict the possibility of lymph node metastasis in DCIS-MI patients and help preoperative or intraoperative clinical decision-making. A retrospective analysis of patients with DCIS-MI in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2019 was performed. The study cohort was randomly divided into a training cohort and a validation cohort at a ratio of 7:3. The risk factors were determined by univariate and multivariate logistic regression analyses in the training cohort, and a nomogram was constructed. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram in the training set and validation set. An independent data cohort was obtained from the Shanghai Jiao Tong University Breast Cancer Database (SJTU-BCDB) for external validation. This study included 3951 female patients from SEER with DCIS-MI, including 244 patients with regional lymph node metastasis, accounting for 6.18% of the total. An independent test set of 323 patients from SJTU-BCDB was used for external validation. According to the multifactorial logistic regression analysis results, age at diagnosis, ethnicity, grade, and surgical modality were included in the prediction model. The areas under the ROC curves (AUCs) were 0.739 (95% CI: 0.702~0.775), 0.732 (95% CI: 0.675~0.788), and 0.707 (95%CI: 0.607-0.807) in the training, validation and external test groups, suggesting that the column line graphs had excellent differentiation. The calibration curves slope was close to 1, and the model's predicted values were in good agreement with the actual values. The DCA curves showed good clinical utility. In this study, we constructed accurate and practical columnar maps with some clinical benefit to predict the likelihood of lymph node metastasis in patients with postoperatively diagnosed DCIS-MI and provide a reference value for specifying treatment strategies.

Dual-Region Computed Tomography Radiomics-Based Machine Learning Predicts Subcarinal Lymph Node Metastasis in Patients with Non-small Cell Lung Cancer.

Noninvasively and accurately predicting subcarinal lymph node metastasis (SLNM) for patients with non-small cell lung cancer (NSCLC) remains challenging. This study was designed to develop and validate a tumor and subcarinal lymph nodes (tumor-SLNs) dual-region computed tomography (CT) radiomics model for predicting SLNM in NSCLC. This retrospective study included NSCLC patients who underwent lung resection and SLNs dissection between January 2017 and December 2020. The radiomic features of the tumor and SLNs were extracted from preoperative CT, respectively. Ninety machine learning (ML) models were developed based on tumor region, SLNs region, and tumor-SLNs dual-region. The model performance was assessed by the area under the curve (AUC) and validated internally by fivefold cross-validation. In total, 202 patients were included in this study. ML models based on dual-region radiomics showed good performance for SLNM prediction, with a median AUC of 0.794 (range, 0.686-0.880), which was superior to those of models based on tumor region (median AUC, 0.746; range, 0.630-0.811) and SLNs region (median AUC, 0.700; range, 0.610-0.842). The ML model, which is developed by using the naive Bayes algorithm and dual-region features, had the highest AUC of 0.880 (range of cross-validation, 0.825-0.937) among all ML models. The optimal logistic regression model was inferior to the optimal ML model for predicting SLNM, with an AUC of 0.727. The CT radiomics showed the potential for accurately predicting SLNM in NSCLC patients. The ML model with dual-region radiomic features has better performance than the logistic regression or single-region models.

Comparison of the prognostic value of lymph node yield, lymph node ratio, and number of lymph node metastases in patients with oral squamous cell carcinoma.

The aim of this study was to assess the prognostic significance of lymph node yield (LNY), lymph node ratio (LNR), and the number of lymph node metastases (LNMs) in patients affected by oral squamous cell carcinoma (OSCC). The study included patients who underwent surgical treatment for primary OSCC. Receiver operating characteristic curves were generated to determine the optimal threshold values. Kaplan-Meier curves were employed, along with the log-rank test, for the analysis of survival. To compare the performance in terms of model fit, we computed Akaike's information criterion (AIC). This study enrolled 429 patients. Prognostic thresholds were determined at 22 for LNY, 6.6% for LNR, and 3 for the number of LNMs. The log-rank test revealed a significant improvement in both overall survival and progression-free survival for patients with a LNR of ≤6.6% or a number of LNMs of ≤3 (p < 0.05). Interestingly, LNY did not demonstrate prognostic significance. The AIC analyses indicated that the number of LNMs is a superior prognostic indicator compared to LNY and LNR. Incorporating LNR or the number of LNMs into the TNM classification has the potential to improve the prognostic value, as in other types of cancers. Particularly, the inclusion of the number of LNMs should be contemplated for future N staging.

Differentiation between normal and metastatic lymph nodes in patients with skin melanoma: Preliminary findings using a DIXON-based whole-body MRI approach

PurposeMetastatic melanoma lymph nodes (MMLns) might be challenging to detect on MR-WBI, as both MMLns and normal lymph nodes (NLns) can show restricted water diffusion. Our purpose is to assess the potential contribution of the DIXON sequence in differentiating MMLns from NLns. Material and methodsWe followed a cohort of 107 patients with stage IIIb/c and IV skin melanoma for 32 months using MR-WBI with DIXON, STIR, and DWI/ADC sequences. We compared signal intensity (SI) values of MMLns and NLns in the four series of the DIXON sequence (in/out-of-phase, fat_only, and water_only series). The fat fraction (SIfat_only/SIin) and the long:short axis ratio of MMLns were calculated. The fat fraction was also calculated in the fatty hila of NLns. ResultsAll MMLns (8 from 7 patients) showed SIout>SIin with a mean fat fraction of 10%. In 40 normal fatty hila (25 patients), the proportion of SIout>SIin was 100% and mean fat fraction was 89% (p<0.001 for fat fraction, Mann-Whitney U-test). In the cortex of NLns, a SIout>SIin pattern was identified in 41/113 cases from 19/40 patients. The median long:short axis ratio in MMLns was 1.13 (range 1.03–1.25). ConclusionThe combination of three features of MMLns (SIout>SIin, low-fat fraction and rounded shape) might hold promise in differentiating NLns from MMLns in patients with skin melanoma. Further research is warranted due to the small number of MMLns in our cohort.

Accurate preoperative prediction of nodal metastasis in papillary thyroid microcarcinoma: Towards optimal management of patients.

To enhance the accuracy in predicting lymph node metastasis (LNM) preoperatively in patients with papillary thyroid microcarcinoma (PTMC), refining the "low-risk" classification for tailored treatment strategies. This study involves the development and validation of a predictive model using a cohort of 1004 patients with PTMC undergoing thyroidectomy along with central neck dissection. The data was divided into a training cohort (n = 702) and a validation cohort (n = 302). Multivariate logistic regression identified independent LNM predictors in PTMC, leading to the construction of a predictive nomogram model. The model's performance was assessed through ROC analysis, calibration curve analysis, and decision curve analysis. Identified LNM predictors in PTMC included age, tumor maximum diameter, nodule-capsule distance, capsular contact length, bilateral suspicious lesions, absence of the lymphatic hilum, microcalcification, and sex. Especially, tumors larger than 7 mm, nodules closer to the capsule (less than 3 mm), and longer capsular contact lengths (more than 1 mm) showed higher LNM rates. The model exhibited AUCs of 0.733 and 0.771 in the training and validation cohorts respectively, alongside superior calibration and clinical utility. This study proposes and substantiates a preoperative predictive model for LNM in patients with PTMC, honing the precision of "low-risk" categorization. This model furnishes clinicians with an invaluable tool for individualized treatment approach, ensuring better management of patients who might be proposed observation or ablative options in the absence of such predictive information.

Head-to-head comparison of 18F-FDG PET/CT and 18F-FDG PET/MRI for lymph node metastasis staging in non-small cell lung cancer: a meta-analysis.

The current meta-analysis aimed to compare the diagnostic performance of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) with 18F-FDG PET/magnetic resonance imaging (MRI) in non-small cell lung cancer (NSCLC) lymph node metastasis staging. We searched the PubMed, Web of Science, and Embase databases for relevant articles between November 1992 and September 2022. Studies evaluating the head-to-head comparison of 18F-FDG PET/CT and 18F-FDG PET/MRI for lymph node metastasis in patients with NSCLC were included. The quality of each study was assessed using the Quality Assessment of Diagnostic Performance Studies-2 tool. The analysis includes six studies with a total of 434 patients. The pooled sensitivity of 18F-FDG PET/CT and 18F-FDG PET/MRI was 0.78 [95% confidence interval (CI): 0.59-0.90] and 0.84 (95% CI: 0.68-0.93), and the pooled specificity was 0.87 (95% CI: 0.72-0.94) and 0.87 (95% CI: 0.80-0.92), respectively. The accuracy of 18F-FDG PET/CT and 18F-FDG PET/MRI was 0.81 (95% CI: 0.71-0.90) and 0.84 (95% CI: 0.75-0.92), respectively. When the pre-test probability was set at 50%, the post-test probability for 18F-FDG PET/CT could increase to 85%, and the post-test probability for 18F-FDG PET/MRI could increase to 87%. 18F-FDG PET/CT and 18F-FDG PET/MRI have similar diagnostic performance in detecting lymph node metastasis in NSCLC. However, the results of this study were from a small sample study, and further studies with larger sample sizes are needed.

Expression of SOX4 Significantly Predicts the Risk of Lymph Node Metastasis for Patients With Early-Stage Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma stands as a notably aggressive malignancy within the digestive system. In cases of early esophageal cancer without lymph node metastasis, endoscopic surgical resection offers a viable alternative, often resulting in improved patient quality of life. However, the paucity of methods to preoperatively ascertain lymph node involvement complicates surgical planning. SOX4 gene was previously found to be highly associated with invasive metastasis in our work through single-cell RNA sequencing on 5 paired tumor/peritumor tissues. This research included the collection of 124 tissue samples from 106 patients (106 tumor and 18 lymph node specimens). Samples were methodically arranged into a tissue microarray and treated with immunohistochemical staining. Statistical analysis was conducted to assess the relationship between them. In the univariate analysis, 3 factors were identified as statistically significant in relation to lymph node metastasis: T category (P = .014), vascular invasion (P < .001), and SOX4 intensity (P = .001). Additionally, when evaluating SOX4 intensity alongside other clinical indicators, SOX4 was shown to independently influence lymph node metastasis. Further, the multivariate analysis revealed that vascular invasion (P < .001) and SOX4 intensity (P = .003) were significantly associated with lymph node metastasis, exhibiting hazard ratios of 10.174 and 7.142, respectively. The results of our study indicate that both SOX4 expression and vascular invasion serve as predictors of lymph node metastasis in patients diagnosed with category T1 esophageal squamous cell carcinoma, underscoring the potential utility of SOX4 in prognostic evaluations.