Lymph Node Status In Patients Research Articles

Development and validation of a diagnostic model for predicting cervical lymph node metastasis in laryngeal and hypopharyngeal carcinoma

ObjectivesThe lymph node status is crucial for guiding the surgical approach for patients with laryngeal and hypopharyngeal carcinoma (LHC). Nonetheless, occult lymph node metastasis presents challenges to assessment and treatment planning. This study seeks to develop and validate a diagnostic model for evaluating cervical lymph node status in LHC patients.Materials and methodsThis study retrospectively analyzed a total of 285 LHC patients who were treated at the Department of Otolaryngology Head and Neck Surgery, Daping Hospital, Army Medical University, from January 2015 to December 2020. Univariate and multivariate logistic regression analyses were employed to construct the predictive model. Discrimination and calibration were used to assess the predictive performance of the model. Decision curve analysis (DCA) was performed to evaluate the clinical utility of the model, and validation was conducted using 10-fold cross-validation, Leave-One-Out Cross Validation, and bootstrap methods.ResultsThis study identified significant predictors of lymph node metastasis in LHC. A diagnostic predictive model was developed and visualized using a nomogram. The model demonstrated excellent discrimination, with a C-index of 0.887 (95% CI: 0.835-0.933). DCA analysis indicated its practical applicability, and multiple validation methods confirmed its fitting and generalization ability.ConclusionThis study successfully established and validated a diagnostic predictive model for cervical lymph node metastasis in LHC. The visualized nomogram provides a convenient tool for personalized prediction of cervical lymph node status in patients, particularly in the context of occult cervical lymph node metastasis, offering valuable guidance for clinical treatment decisions.

228 (PB-135) Poster - Correlation between plasma D-Dimer and Factor VIII levels with clinical stage and axillary lymph node status in patients presenting with operable breast cancer

228 (PB-135) Poster - Correlation between plasma D-Dimer and Factor VIII levels with clinical stage and axillary lymph node status in patients presenting with operable breast cancer

Development and validation of an ultrasound‑based radiomics nomogram to predict lymph node status in patients with high-grade serous ovarian cancer: a retrospective analysis

BackgroundDespite advances in medical imaging technology, the accurate preoperative prediction of lymph node status remains challenging in ovarian cancer. This retrospective study aimed to investigate the feasibility of using ultrasound-based radiomics combined with preoperative clinical characteristics to predict lymph node metastasis (LNM) in patients with high-grade serous ovarian cancer (HGSOC).ResultsPatients with 401 HGSOC lesions from two institutions were enrolled: institution 1 for the training cohort (n = 322) and institution 2 for the external test cohort (n = 79). Radiomics features were extracted from the three preoperative ultrasound images of each lesion. During feature selection, primary screening was first performed using the sample variance F-value, followed by recursive feature elimination (RFE) to filter out the 12 most significant features for predicting LNM. The radscore derived from these 12 radiomic features and three clinical characteristics were used to construct a combined model and nomogram to predict LNM, and subsequent 10-fold cross-validation was performed. In the test phase, the three models were tested with external test cohort. The radiomics model had an area under the curve (AUC) of 0.899 (95% confidence interval [CI]: 0.864–0.933) in the training cohort and 0.855 (95%CI: 0.774–0.935) in the test cohort. The combined model showed good calibration and discrimination in the training cohort (AUC = 0.930) and test cohort (AUC = 0.881), which were superior to those of the radiomic and clinical models alone.ConclusionsThe nomogram consisting of the radscore and preoperative clinical characteristics showed good diagnostic performance in predicting LNM in patients with HGSOC. It may be used as a noninvasive method for assessing the lymph node status in these patients.

Prediction of lymph node status in patients with surgically treated head and neck squamous cell carcinoma via neck lavage cytology: A pilot study.

Neck dissection is a standardized surgical procedure for patients with head and neck squamous cell carcinoma (HNSCC) and plays a critical role in the choice of adjuvant treatment based on histopathological findings. Saline irrigation is routinely performed at the end of surgery. However, this irrigant is not used for diagnostic purposes. Intraoperative irrigation of the neck dissection wound was performed in 56 patients with HNSCC (N=93 neck dissections), and the cytological suspension obtained was processed via the liquid-based cytology (LBC) technique, Papanicolaou staining, and immunocytochemical staining. Microscopic preparations were screened for the presence of tumor cells and classified as positive, borderline, or negative. These results were correlated with the histopathological and clinical data. Neck lavage LBC demonstrated high diagnostic value in detecting lymph node metastases (N+) with extracapsular spread (ECS), with a specificity, sensitivity, negative predictive value, and positive predictive value of 93.1%, 100%, 100%, and 80%, respectively. Tumor cells were detected in 4.8% of N- cases, 20% of N+ cases without ECS, and 100% of N+ cases with ECS. Receiver operating characteristic curve analysis showed an area under the curve of 0.8429 for the prediction of N+ (p<.0001) and 0.9658 for the prediction of N+ with ECS (p<.0001). Differential lavage cytology can provide valid and rapid information on the lymph node status in patients with HNSCC and showed an excellent correlation with histopathology. Thus, neck lavage LBC may facilitate faster and more reasonable planning of adjuvant treatment and help improve the therapeutic management of patients with HNSCC.

The role of adenocarcinoma subtypes and immunohistochemistry in predicting lymph node metastasis in early invasive lung adenocarcinoma

BackgroundIdentifying lymph node metastasis areas during surgery for early invasive lung adenocarcinoma remains challenging. The aim of this study was to develop a nomogram mathematical model before the end of surgery for predicting lymph node metastasis in patients with early invasive lung adenocarcinoma.MethodsIn this study, we included patients with invasive lung adenocarcinoma measuring ≤ 2 cm who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University from January 2020 to January 2022. Preoperative biomarker results, clinical features, and computed tomography characteristics were collected. The enrolled patients were randomized into a training cohort and a validation cohort in a 7:3 ratio. The training cohort was used to construct the predictive model, while the validation cohort was used to test the model independently. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. The prediction model and nomogram were established based on the independent risk factors. Recipient operating characteristic (ROC) curves were used to assess the discrimination ability of the model. Calibration capability was assessed using the Hosmer–Lemeshow test and calibration curves. The clinical utility of the nomogram was assessed using decision curve analysis (DCA).ResultsThe overall incidence of lymph node metastasis was 13.23% (61/461). Six indicators were finally determined to be independently associated with lymph node metastasis. These six indicators were: age (P < 0.001), serum amyloid (SA) (P = 0.008); carcinoma antigen 125 (CA125) (P = 0. 042); mucus composition (P = 0.003); novel aspartic proteinase of the pepsin family A (Napsin A) (P = 0.007); and cytokeratin 5/6 (CK5/6) (P = 0.042). The area under the ROC curve (AUC) was 0.843 (95% CI: 0.779–0.908) in the training cohort and 0.838 (95% CI: 0.748–0.927) in the validation cohort. the P-value of the Hosmer–Lemeshow test was 0.0613 in the training cohort and 0.8628 in the validation cohort. the bias of the training cohort corrected C-index was 0.8444 and the bias-corrected C-index for the validation cohort was 0.8375. demonstrating that the prediction model has good discriminative power and good calibration.ConclusionsThe column line graphs created showed excellent discrimination and calibration to predict lymph node status in patients with ≤ 2 cm invasive lung adenocarcinoma. In addition, the predictive model has predictive potential before the end of surgery and can inform clinical decision making.

Diagnostic role of F-18 FDG PET/CT in determining preoperative Lymph node status of patients with rectal cancer: a meta-analysis.

To obtain performance values of PET/CT for determining the nodal status of rectal cancer. A comprehensive literature search was performed on PubMed and Embase for original diagnostic accuracy studies on the diagnostic performance of PET-CT for detection of LN metastasis in rectal cancer. The QUADAS-2 was used to evaluate the methodological quality of each study. Pooled sensitivity, specificity, and AUC were calculated to estimate the diagnostic role of PET/CT using a random-effects model. A subgroup analysis was performed to investigate the influence of different parameters on diagnostic performance. A total of 15 studies and 1209 patients were included. A publication bias was observed. The pooled sensitivity, specificity, and AUC for PET/CT was 0.62 (95% CI 0.49, 0.74), 0.94 (95% CI 0.87, 0.97), and 0.87 (95% CI 0.83-0.89), respectively. Per-node basis yields higher accuracy than per-patient basis, with pooled sensitivities of 0.65 (95% CI 0.50-0.79) vs. 0.56 (95% CI 0.36-0.77) and specificities of 0.96 (95% CI 0.92-1.00) vs. 0.88 (95% CI 0.76-1.00), but there were no significant differences in diagnostic accuracy. PET/CT has high specificity but moderate sensitivity for the detection of LN metastasis in rectal cancer. The current data suggests that the diagnostic capabilities of this method is limited due to its moderate sensitivity.

The prognostic value of radiological and pathological lymph node status in patients with cervical cancer who underwent neoadjuvant chemotherapy and followed hysterectomy

To investigate the prognostic value of lymph node status in patients with cervical cancer (CC) patients who underwent neoadjuvant chemotherapy (NACT) and followed hysterectomy. Patients in two referral centers were retrospectively analyzed. The baseline tumor size and radiological lymph node status (LNr) were evaluated on pre-NACT MRI. Tumor histology, differentiation and pathological lymph node status (LNp) were obtained from post-operative specimen. The log-rank test was used to compare survival between patient groups. Cox proportional hazards regression models were employed to estimate the hazard ratio (HR) of various factors with progression-free survival (PFS) and overall survival (OS). A total of 266 patients were included. Patients with 2018 FIGO IIIC showed worse PFS compared to those with FIGO IB-IIB (p < 0.001). The response rate in patients with LNp(−) was 64.1% (134/209), significantly higher than that of 45.6% (26/57) in patients with LNp( +) (p = 0.011). Multivariate Cox analysis identified the main independent predictors of PFS as LNp( +) (HR = 3.777; 95% CI 1.715–8.319), non-SCC (HR = 2.956; 95% CI 1.297–6.736), poor differentiation (HR = 2.370; 95% CI 1.130–4.970) and adjuvant radiation (HR = 3.266; 95% CI 1.183–9.019). The interaction between LNr and LNp regarding PFS were significant both for univariate and multivariate (P = 0.000171 and 1.5357e−7 respectively). In patients with LNr( +), a significant difference in PFS was observed between patients with LNp(−) and LNp( +) (p = 0.0027). CC patients with FIGO 2018 stage IIIC who underwent NACT and followed hysterectomy had worse PFS compared to those with IB-IIB. LNp( +), non-SCC, poor differentiation and adjuvant radiation were independent risk factors for PFS. The adverse prognostic value of LNp( +) was more significant in patients with LNr( +).

Non-invasive prediction model of axillary lymph node status in patients with early-stage breast cancer: a feasibility study based on dynamic contrast-enhanced-MRI radiomics.

Accurate axillary evaluation plays an important role in prognosis and treatment planning for breast cancer. This study aimed to develop and validate a dynamic contrast-enhanced (DCE)-MRI-based radiomics model for preoperative evaluation of axillary lymph node (ALN) status in early-stage breast cancer. A total of 410 patients with pathologically confirmed early-stage invasive breast cancer (training cohort, N = 286; validation cohort, N = 124) from June 2018 to August 2022 were retrospectively recruited. Radiomics features were derived from the second phase of DCE-MRI images for each patient. ALN status-related features were obtained, and a radiomics signature was constructed using SelectKBest and least absolute shrinkage and selection operator regression. Logistic regression was applied to build a combined model and corresponding nomogram incorporating the radiomics score (Rad-score) with clinical predictors. The predictive performance of the nomogram was evaluated using receiver operator characteristic (ROC) curve analysis and calibration curves. Fourteen radiomic features were selected to construct the radiomics signature. The Rad-score, MRI-reported ALN status, BI-RADS category, and tumour size were independent predictors of ALN status and were incorporated into the combined model. The nomogram showed good calibration and favourable performance for discriminating metastatic ALNs (N + (≥1)) from non-metastatic ALNs (N0) and metastatic ALNs with heavy burden (N + (≥3)) from low burden (N + (1-2)), with the area under the ROC curve values of 0.877 and 0.879 in the training cohort and 0.859 and 0.881 in the validation cohort, respectively. The DCE-MRI-based radiomics nomogram could serve as a potential non-invasive technique for accurate preoperative evaluation of ALN burden, thereby assisting physicians in the personalized axillary treatment for early-stage breast cancer patients. This study developed a potential surrogate of preoperative accurate evaluation of ALN status, which is non-invasive and easy-to-use.

Identification of Optimal Parameters for Assessing Lymph Node Status of Patients with Esophageal Squamous Cell Carcinoma After Neoadjuvant Chemoradiotherapy.

This study aimed to compare the prognostic discrimination power of pretreatment pathologic N stage (prepN), lymph node tumor regression grade (LNTRG), and posttreatment pathologic N (ypN) category for esophageal squamous cell carcinoma (ESCC) patients who received neoadjuvant chemoradiotherapy (nCRT) plus surgery. The study reviewed 187 ESCC patients from two medical centers who underwent nCRT plus surgery. Pathologic LNTRG was defined by the proportion of viable tumor area within the tumor bed in lymph nodes (LNs). An average LNTRG then was calculated by averaging the tumor regression grade (TRG) score of all resected LNs. Lymph nodes containing regression changes or vital tumor cells were used for interpretation of the prepN stage, which reflects the estimated number of originally involved LNs. The ypN, prepN, and LNTRG categories had significant prognostic stratification power (p < 0.001, log-rank test). Multivariable cox regression showed that all three categories were independent prognostic factors of disease-free survival (DFS) (p < 0.05). The LNTRG category showed a better prognostic value for DFS prediction than the ypN and prepN categories (Akaike information criterion [AIC]: LNTRG [933.69], ypN [937.56], prepN [937.45]). Additionally, the superior predictive capacity of the LNTRG category was demonstrated by decision curve analysis. Similar results were discovered for patients with remaining diseased LNs. The three staging categories had prognostic relevance for DFS, with the LNTRG category seeming to have better prognostic indication power. Comprehensive consideration of the ypN status, prepN status, and LN regression may allow for better prognostic stratification of patients.

ASO Visual Abstract: Identification of Optimal Parameters to Assess the Lymph Node Status of Patients with Esophageal Squamous Cell Carcinoma After Neoadjuvant Chemoradiotherapy.

ASO Visual Abstract: Identification of Optimal Parameters to Assess the Lymph Node Status of Patients with Esophageal Squamous Cell Carcinoma After Neoadjuvant Chemoradiotherapy.

Diagnostic accuracy of contrast-enhanced computed tomography in assessing cervical lymph node status in patients with oral squamous cell carcinoma

ObjectiveAccurate preoperative prediction of lymph node (LN) status plays a pivotal role in determining the extension of neck dissection (ND) required for patients with oral squamous cell carcinoma (OSCC). This study aims to evaluate the diagnostic accuracy of contrast-enhanced computed tomography (CT) in detecting LN metastases (LNMs) and to explore clinicopathological factors associated with its reliability.MethodsData from 239 patients with primary OSCC who underwent preoperative CT and subsequent radical surgery involving ND were retrospectively reviewed. Suspicious LNs were categorized into three groups: accentuated (< 10 mm), enlarged (≥ 10 mm), and melted. Statistical analysis encompassing correlation and comparative analysis, and determination of sensitivity, specificity, PPV, and NPV were performed.ResultsOverall, sensitivity was significantly higher in the accentuated LNs group (83.54%) compared to the melted LNs group (39.24%, p < 0.05, t test). Conversely, specificity was significantly higher in the melted LNs group (98.19%) compared to the accentuated LNs group (55.15%, p < 0.05, t test). Accentuated LNs exhibited a false negative rate of 13.00%. False positive rates were 51.80%, 30.26% and 8.82%, respectively. Diagnostic accuracy for detecting LNMs in level IIa and IIb exceeded that of level III. Patients with solely accentuated LNs were more likely to have a small, well-differentiated tumor. However, no distinctions emerged in terms of the occurrence of T4 tumors among the three groups.ConclusionCT proves sufficient to predict LNMs in patients with OSCC. Looking ahead, the potential integration of artificial intelligence and deep learning holds promise to further enhance the reliability of CT in LNMs detection. However, this prospect necessitates further investigation.

The additive diagnostic role of diffusion-weighted magnetic resonance and chemical shift imaging (CSI) in differentiation between malignant and benign cervical lymph nodes

BackgroundImaging with conventional MRI plays a pivotal role in differentiation between benign and malignant nodes, which is crucial for choice of therapeutic plan. The purpose of this study was to evaluate the role of diffusion-weighted imaging DWI MRI and chemical shift imaging (CSI) (in and out of phase) in differentiation between malignant and benign cervical lymph nodes.Patients and methodsWe prospectively reviewed MR images of 31 patients presented to ENT department by cervical adenopathy. Imaging parameters documented included: morphology, ADC values and in-phase to out-of-phase (Ip/Op) ratios and signal drop in out-of-phase images.ResultsADC values of the benign and malignant nodes were 1.06 ± 0.25 and 0.85 ± 0.24, respectively. A statistically significant difference was found between the two groups (p = 0.041). An ADC value of 0.9 × 10−3 mm2/s was concluded to be the best cutoff value for differentiating benign from malignant LNs. The mean values of in/out-of-phase ratios of benign and malignant lymph nodes were 0.75 ± 0.16 and 0.96 ± 0.06, respectively. A statistically significant difference was found between the two groups (p = 0.001). The mean in/out-of-phase ratio of the benign nodes was significantly lower than that of the malignant nodes.ConclusionsCSI can differentiate between malignant and benign lymph nodes with a cutoff value of in/out-phase ratio of 0.9. Also, adding the diffusion-ADC map can prefer the same value with a cutoff value of 0.9 × 10−3 cm2/s. Adding CSI sequence to conventional MRI in examining the nodal status in patients with head and neck primary can enhance diagnostic accuracy of the examination.

Predicting Lymph Node Metastasis From Primary Cervical Squamous Cell Carcinoma Based on Deep Learning in Histopathologic Images

We developed a deep learning framework to accurately predict the lymph node status of patients with cervical cancer based on hematoxylin and eosin–stained pathological sections of the primary tumor. In total, 1524 hematoxylin and eosin–stained whole slide images (WSIs) of primary cervical tumors from 564 patients were used in this retrospective, proof-of-concept study. Primary tumor sections (1161 WSIs) were obtained from 405 patients who underwent radical cervical cancer surgery at the Fudan University Shanghai Cancer Center (FUSCC) between 2008 and 2014; 165 and 240 patients were negative and positive for lymph node metastasis, respectively (including 166 with positive pelvic lymph nodes alone and 74 with positive pelvic and para-aortic lymph nodes). We constructed and trained a multi-instance deep convolutional neural network based on a multiscale attention mechanism, in which an internal independent test set (100 patients, 228 WSIs) from the FUSCC cohort and an external independent test set (159 patients, 363 WSIs) from the Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma cohort of the Cancer Genome Atlas program database were used to evaluate the predictive performance of the network. In predicting the occurrence of lymph node metastasis, our network achieved areas under the receiver operating characteristic curve of 0.87 in the cross-validation set, 0.84 in the internal independent test set of the FUSCC cohort, and 0.75 in the external test set of the Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma cohort of the Cancer Genome Atlas program. For patients with positive pelvic lymph node metastases, we retrained the network to predict whether they also had para-aortic lymph node metastases. Our network achieved areas under the receiver operating characteristic curve of 0.91 in the cross-validation set and 0.88 in the test set of the FUSCC cohort. Deep learning analysis based on pathological images of primary foci is very likely to provide new ideas for preoperatively assessing cervical cancer lymph node status; its true value must be validated with cervical biopsy specimens and large multicenter datasets.

Can inflammatory indices predict sentinel lymph node status in patients with early-stage breast cancer?

Breast cancer research has focused on the early detection and treatment of breast cancer. Axillary lymph node status is essential for primary breast cancer staging, recurrence, and survival. The current quest for precision medicine is to identify predictive markers that offer the advantage of individualized treatment options. This study aimed to investigate the value of inflammatory indices in predicting positive sentinel nodes in breast cancer. We studied 602 patients with early-stage breast cancer who underwent sentinel lymph node biopsies (SLNB) at the Bülent Ecevit University General Surgery Clinic. We obtained data, including the clinical and demographic characteristics of the patients, such as age, histological type, and sentinel lymph nodes. Neutrophil, lymphocyte, platelet, and monocyte counts were obtained from preoperative complete blood count test data from the patient registry. The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic inflammatory index (SII), and sentinel lymph node biopsy were analyzed. Sentinel LAP was negative in 391 (65%) patients and positive in 211 (35%). In the receiver operating characteristic curve analysis, no significant difference was found between SLNB positivity and negativity in terms of NLR, PLR, LMR, or SII. In contrast to previous research, NLR, PLR, LMR, or SII did not affect SLNB positivity prediction in our study.

Role of 18F-FDG PET/CT in evaluating lymph node status in patients with head and neck squamous cell carcinoma.

The presence of cervical lymph node metastases (CLNM) at diagnosis is one of the most relevant negative prognostic factors in patients with head and neck squamous cell carcinoma (HNSCC). The aim of this study was to analyse 2-deoxy-2[18F]fluoro-D-glucose (FDG) PET/CT findings for the identification of primary tumours and CLNM in a sample of patients affected by HNSCC. Moreover, a maximum standardised uptake value (SUVmax) threshold for the detection of CLNM was estimated. Clinical variables (i.e. smoking and alcohol habits), and tumour features (i.e. EBV and HPV positivity) were also evaluated in relation to FDG PET/CT findings. We retrospectively analysed patients who underwent FDG PET/CT for HNSCC staging between 2015-2020 at the University Hospital of Ferrara. All patients had cytological or histological confirmation of suspected cervical lymph nodes. In total, 65 patients were enrolled (53 males, 12 females, median age 65.7 years). CLNM of patients with smoking habit had significantly higher SUVmax values than those of patients with previous smoking habit and non-smokers (p = 0.04). p16 positive HNSCC demonstrated a trend for higher SUVmax values on CLNM, in comparison to p16 negative tumours (p = 0.089). ROC curve analysis identified 5.8 as the best cut-off value of SUVmax for the detection of CLNM (AUC = 0.62, sensitivity 71.4% and specificity 72.7%). FDG PET/CT is a useful tool to evaluate CLNM in patients with HNSCC, particularly in those with smoking habit and p16 positive disease. A SUVmax cut-off of 5.8, combined with the use of conventional radiological investigations, may represent a useful tool in the identification of CLNM.

Use of Carbon Nanoparticles to Improve the Efficiency of Harvesting Lymph Nodes in Rectal Cancer.

The accurate determination of lymph node status in patients with rectal cancer requires harvesting a certain number of lymph nodes. This study investigated whether using carbon nanoparticles (CNs) could improve the efficiency of harvesting lymph nodes in rectal cancer patients. Data from patients with rectal cancer treated with radical resection were collected from Nanfang Hospital between January 2014 and June 2021. Patients in the CN group received a CN suspension 1 day before surgery, which was endoscopically injected around the tumor. A 1:1 case-matched study was performed using the propensity score. The efficiency of harvesting lymph nodes was investigated by comparing the number of total nodes, total time, and percentage of nodes <5mm in size between the CN and non-CN groups. A total of 768 patients were included, with 246 patients who underwent CN injection and 522 patients who did not. After matching, 246 pairs of patients were analyzed. After matching, the number of total nodes of each sample was significantly higher in the CN group than in the non-CN group ( P <0.001). The total time for node detection ( P <0.001) was significantly shorter in the CN group. The percentage of nodes <5mm in size was increased significantly in the CN group ( P <0.001). Among patients with clinical staging I/II, the difference in positive LNs was significant (21.79% vs. 11.95%, P =0.029). The application of CNs improved the efficiency of harvesting lymph nodes during rectal cancer surgery.

Prediction of lymph node status in patients with early-stage cervical cancer based on radiomic features of magnetic resonance imaging (MRI) images

BackgroundLymph node metastasis is an important factor affecting the treatment and prognosis of patients with cervical cancer. However, the comparison of different algorithms and features to predict lymph node metastasis is not well understood. This study aimed to construct a non-invasive model for predicting lymph node metastasis in patients with cervical cancer based on clinical features combined with the radiomic features of magnetic resonance imaging (MRI) images.MethodsA total of 180 cervical cancer patients were divided into the training set (n = 126) and testing set (n = 54). In this cross-sectional study, radiomic features of MRI images and clinical features of patients were collected. The least absolute shrinkage and selection operator (LASSO) regression was used to filter the features. Seven machine learning methods, including eXtreme Gradient Boosting (XGBoost), Logistic Regression, Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting Decision Tree (GBDT) are used to build the models. Receiver operating characteristics (ROC) curve and area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the performance of the models.ResultsOf these 180 patients, 49 (27.22%) patients had lymph node metastases. Five of the 122 radiomic features and 3 clinical features were used to build predictive models. Compared with other models, the MNB model was the most robust, with its AUC, specificity, and accuracy on the testing set of 0.745 (95%CI: 0.740–0.750), 0.900 (95%CI: 0.807–0.993), and 0.778 (95%CI: 0.667–0.889), respectively. Furthermore, the AUCs of the MNB models with clinical features only, radiomic features only, and combined features were 0.698 (95%CI: 0.692–0.704), 0.632 (95%CI: 0.627–0.637), and 0.745 (95%CI: 0.740–0.750), respectively.ConclusionThe MNB model, which combines the radiomic features of MRI images with the clinical features of the patient, can be used as a non-invasive tool for the preoperative assessment of lymph node metastasis.

Predictive nomogram for central lymph node metastasis in papillary thyroid microcarcinoma based on pathological and ultrasound features.

Central lymph node metastases (CLNM) in papillary thyroid microcarcinoma (PTMC) are common, but management through prophylactic central lymph node dissection (pCLND) remains controversial. In this study, the independent predictors of CLNM in PTMC were retrospectively studied based on ultrasound and pathological data, and we aim to establish the prediction model to predict CLNM in PTMC. This study included a total of 1,506 patients who underwent thyroid surgery for PTMC at the First Affiliated Hospital of Chongqing Medical University from 2015 to 2018. Ultrasound and clinicopathological features were summarized and analyzed. Univariate and multivariate analyses were performed to determine the risk factors associated with CLNM. The prediction model is established and verified according to the multivariate analysis results. The Kaplan-Meier curve was used to evaluate the effect of CLNM on survival. The CLNM rate was 44.5% (670/1,506). Multivariate analysis showed that men, younger age, smaller diameter, ETE, microcalcification, without Hashimoto's thyroiditis, and multifocal were independent risk predictors of CLNM. Nomogram has a good discriminative ability (C-index: 0.755 in the validation group), and the calibration effect is good. In the DCA curve, the CLNM prediction model performed better net benefit given any high-risk thresholds. The median follow-up time was 30 months (12-59 months), 116 cases were lost, and the follow-up rate was 92.8% (1,506/1,622). Of the 1,506 patients included, 12 (0.8%) experienced recurrence. The likelihood of CLNM can be objectively quantified before surgery by using this reliable and accurate nomogram that combines preoperative ultrasound with clinicopathological features. Clinicians can use this nomogram to assess central lymph node status in patients with PTMC and consider prophylactic CND in patients with high scores.

Magnetic resonance imaging in preoperative assessment of the mesorectal nodal status of patients with rectal cancer – Can it be trusted?

PurposeMagnetic resonance imaging (MRI) is a fundamental diagnostic modality for the evaluation of primary rectal cancer, but MRI assessment of nodal involvement remains a confounding factor. MethodThis prospective cohort study was conducted to investigate the accuracy of preoperative MRI in the assessment of nodal status by comparing histopathology reports to MRI findings on a node-by-node basis in 69 patients with rectal cancer. ResultsPrimary surgery was performed in 40 (58.0%) patients; 29 (42.0%) study patients underwent neoadjuvant chemoradiotherapy (CRT). Histopathological examination revealed T1 tumour in 8 (11.6%) patients, T2 tumour in 30 (43.5%), and T3 tumour in 25 (36.2%). In total, 897 lymph nodes (LNs) have been harvested (13.1 ± 5.4 LNs per specimen). There were 77 MRI-suspicious LNs, 21 (27.3%) of which were histologically proven malignant.The sensitivity of MRI for assessing nodal involvement was 51.2% and specificity 93.4%. Of the 28 patients with MRI-suspicious LNs the diagnosis was correct in 42.8%. The MRI accuracy was 33.3% in “primary surgery” subgroup (n = 18, malignant LNs found in 6 patients). Diagnosis of MRI-negative LNs was correct in 90.2% of study patients; malignant nodes were found in 9.8% of patients initially classified as cN0. ConclusionsMRI prediction of nodal status in patients with rectal cancer has very low accuracy. Decisions regarding neoadjuvant CRT should not be based on MRI assessment of nodal status, but on the MRI evaluation of tumour depth invasion (T stage and relationship between the tumour and mesorectal fascia).

Prognostic and predictive values of the grading system of lymph node status in patients with advanced-stage gastric cancer.

Lymph node metastasis is one of the most important prognostic factors of gastric cancer. However, the effect of germinal centers in lymph nodes on the prognosis of patients with gastric cancer has not been reported. This study aimed to investigate the contribution of germinal center generation to prognostic parameters and clinicopathological significance in gastric cancer. We retrospectively reviewed gastric cancer patients who underwent surgery from October 2012 to June 2022. We analyzed 5484 lymph nodes (210 patients) and calculated the lymph node metastasis rate (LNMR) and the proportion of non-metastatic lymph nodes containing three or more germinal centers (NML-GCP). Using a grading system that incorporated LNMR and NML-GCP. The tumors were classified into three groups based on this system, which was found to be significantly associated with prognosis. The TNM stage and grading system of lymph node status were independent risk factors for overall survival (OS) and disease-free survival (DFS). The 5-year OS rates for patients with advanced gastric cancer were 85.07% (n=50), 58.34% (n=42), and 24.44% (n=21) for Grades 1, 2, and 3, respectively (p<0.0001). The 5-year DFS rates were 65.32% (n=58), 40.85% (n=51), and 5.88% (n=34), respectively (p<0.0001). Patients with Grade 1 advanced gastric cancer had higher 5-year OS and DFS rates compared to those with Grade 2 or 3 in TNM stage II and III. Furthermore, the 5-year OS and DFS rates differed significantly among patients with different grades of advanced gastric cancer who received chemotherapy (p<0.0001). These findings suggest that the grading system may be valuable for predicting prognosis and guiding clinical management in patients with gastric cancer, and provides good prognostic stratification for OS and DFS in patients with TNM stage II and III.