Predicting Occult Lymph Node Metastasis Research Articles

Occult lymph node metastasis prediction in non-small cell lung cancer based self-supervised pretrained and hyperbolic theory

Predicting occult lymph node metastasis in non-small cell lung cancer (NSCLC) patients is pivotal for tailoring appropriate surgical and therapeutic interventions. This prognostic factor remains underexplored, largely due to the intricate variability of occult lymph node characteristics and the absence of a pathologically confirmed predictive dataset. Addressing this gap, we retrospectively assembled a dataset of occult lymph node metastases (TJ-OLNM) from NSCLC patients who underwent chest Computed Tomography (CT) scans at Tongji Hospital, Tongji University from 2016 to 2021. Utilizing this dataset, we developed a novel self-supervised learning model, the Occult Lymph Node Metastasis Network (OLNM-Net), which leverages hyperbolic metric few-shot learning to enhance the prediction accuracy of occult metastases. Our comprehensive evaluations demonstrate that OLNM-Net significantly outperforms existing models in predicting occult lymph node metastasis, offering new insights into the preoperative assessment of NSCLC and advancing the application of machine learning in medical diagnostics.

A clinical nomogram for predicting occult lymph node metastasis in patients with non-small-cell lung cancer ≤2 cm.

Sublobar resection has been shown to be feasible for non-small-cell lung cancers (NSCLC) <2 cm in size based on several prospective studies. However, the prognosis of clinical N0 patients who experience an N-stage upgrade after surgery [known as occult lymph node metastasis (OLM)] may be worse. The ability of predict OLM in patients eligible for sublobar resection remains a controversial issue. Patients with NSCLC ≤2 cm in diameter and containing a solid component who underwent surgical treatment at the Affiliated Hospital of Qingdao University were retrospectively enrolled, and 1:1 case matching was performed. The risk factors were identified through logistic regression analyses and theoretical criteria, followed by the development of a nomogram that was evaluated using 200 iterations of 10-fold cross-validation. After case matching, 130 pairs of patients were selected for modelling. According to the multivariable logistic regression analysis, the carcinoembryonic antigen level, consolidation tumour ratio, mean computed tomography number and tumour margin were included in the nomogram. The cross-validated average area under the receiver operating characteristic curve was found to be 0.86. Furthermore, calibration curve and decision curve analyses demonstrated the excellent predictive accuracy and clinical utility of the nomogram respectively. By utilizing accessible characteristics, we developed a nomogram that predicts the probability of OLM in patients with NSCLC ≤2 cm with a solid component. Risk stratification with this nomogram could aid in surgical method decision-making. Not applicable.

Predicting Regional Recurrence and Prognosis in Stereotactic Body Radiation Therapy-Treated Clinical Stage I Non-small Cell Lung Cancer Using a Radiomics Model Constructed With Surgical Data

BackgroundRisk stratification of regional recurrence (RR) is clinically important in the design of adjuvant treatment and surveillance strategies in patients with clinical stage I non-small cell lung cancer (NSCLC) treated with stereotactic body radiotherapy (SBRT). PurposeTo develop a radiomics model predicting occult lymph node metastasis (OLNM) using surgical data and apply it to the prediction of RR in SBRT-treated early-stage NSCLC patients. MethodsPatients with clinical stage I NSCLC who underwent curative surgery with systematic lymph node dissection from January 2013 to December 2018 (the training cohort) and from January 2019 to December 2020 (the validation cohort) were included. A pre-operative CT-based radiomics model, a clinical feature model, and a fusion model predicting OLNM were constructed. The performance of the three models was quantified and compared in the training and validation cohorts. Subsequently, the radiomics model was used to predict RR in a cohort of consecutive SBRT-treated early-stage NSCLC patients from two academic medical centers. ResultsA total of 769 patients were included. Eight CT features were identified in the radiomics model, achieving areas under the curves (AUCs) of 0.85 (95% CI 0.81-0.89) and 0.83 (95% CI 0.80-0.88) in the training and validation cohorts, respectively. Nevertheless, adding clinical features did not improve the performance of the radiomics model. With a median follow-up of 40.0 (95% CI 35.2-44.8) months, 32 of the 213 patients in the SBRT cohort developed RR and those in the high-risk group based on the radiomics model had a higher cumulative incidence of RR (p<0.001) and shorter regional recurrence-free survival (p=0.02), progression-free survival (p=0.004) and overall survival (p=0.006) than those in the low-risk group. ConclusionThe radiomics model based on pathologically confirmed data effectively identified patients with ONLM, which may be useful in the risk stratification among SBRT-treated patients with clinical stage I NSCLC.

Computed tomography characteristics of cN0 primary non-small cell lung cancer predict occult lymph node metastasis

Computed tomography characteristics of cN0 primary non-small cell lung cancer predict occult lymph node metastasis

Baseline heterogeneity of myeloid and stroma cells to predict pCR, baseline lymph node (LN) metastasis, and 1-year EFS in locally advanced ESCC with neoadjuvant immunotherapy: Final results from a single-arm, phase II clinical trial (SEEK-01).

e14661 Background: Response of neoadjuvant immunotherapy can vary among patients in locally advanced ESCC, with the lack of a promising biomarker which need fully capture the heterogeneity of TME. Now, based on final results of the SEEK-01 study (2023 ASCO, P#4047), we have, for the first time, established a precise correlation from baseline TME to pathological response, baseline LN status, and 1-year EFS. Methods: In SEEK-01 study, 22 locally advanced ESCC patients (cT2-4NxM0) were enrolled. Following neoadjuvant chemotherapy (paclitaxel-albumin+carboplatin) combined with tislelizumab, patients underwent minimal invasive esophagectomy (MIE). Fresh tumor tissues were obtained via endoscopy prior to neoadjuvant therapy for scRNAseq analysis. Pathological response was evaluated using the irRVT score, leading to classification into pCR (irRVT=0%), MPR (0&lt;irRVT&lt;10%), and non-responder (irRVT&gt;60%). Baseline LN status was determined based on pathological diagnosis after surgery. We identified subclusters of tumor-associated myeloid cells and stroma cells that significantly predicted pathological response, baseline LN metastasis and recurrence within 1 year. Prediction model of efficacy for neoadjuvant immunotherapy was developed using baseline expression ratios of differential subclusters. Results: Among 22 enrolled patients, 18 completed neoadjuvant therapy and MIE. 4 achieved pCR, 2 got MPR and 6 were non-responder. 12 of 18 proved baseline LN metastasis. 5 of 18 suffered recurrence within 1 year. Myeloid cells, cancer-associated fibroblast (Caf) and endothelium (Endo) shew heterogeneity that can predict pathological response, baseline LN metastasis or recurrence within 1 year. Upgrade of CXCL9+ tumor-associated-macrophage (TAM), LAMP3+mregDC3 was a strong predictor of pCR, while the upgrade of STAT1+TAM, C1q+TAM, IL1b+TAM, IL1b+ Neutrophil, C1q+ Neutrophil was a solid biomarker of non-responder. Besides, presence of PTGS1+Caf, RGS5+Caf, S100B+Caf, RND1+Endo and S100+Endo highly correlated with baseline LN metastasis and recurrence within 1 year, which was better than PET-CT. On this basis, we constructed predictive models for pathological response. With further validated by public scRNAseq and TCGA database, it showed excellent predictive ability and robustness. Conclusions: The final results of the SEEK-01 study demonstrate the differential baseline infiltration of myeloid cells that can predict pathological response of neoadjuvant immunotherapy. The characteristic changes in Caf and Endo in TME can predict occult LN metastasis and 1-year EFS. A predictive model for efficacy of neoadjuvant immunotherapy in locally advanced ESCC was developed to facilitate individualized and precise neoadjuvant therapy before treatment. Clinical trial information: NCT05807542 .

MRI-based deep learning and radiomics for prediction of occult cervical lymph node metastasis and prognosis in early-stage oral and oropharyngeal squamous cell carcinoma: a diagnostic study.

The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20-30% in early-stage oral cancer and oropharyngeal cancer. There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to help surgeons make precise treatment decisions. To construct and evaluate a preoperative diagnostic method to predict OCLNM in early-stage oral and oropharyngeal squamous cell carcinoma (OC and OP SCC) based on deep learning features (DLFs) and radiomics features. A total of 319 patients diagnosed with early-stage OC or OP SCC were retrospectively enrolled and divided into training, test and external validation sets. Traditional radiomics features and DLFs were extracted from their MRI images. The least absolute shrinkage and selection operator (LASSO) analysis was employed to identify the most valuable features. Prediction models for OCLNM were developed using radiomics features and DLFs. The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC), decision curve analysis (DCA), and survival analysis. Seventeen prediction models were constructed. The Resnet50 deep learning (DL) model based on the combination of radiomics and DL features achieves the optimal performance, with AUC values of 0.928 (95% CI: 0.881-0.975), 0.878 (95% CI: 0.766-0.990), 0.796 (95% CI: 0.666-0.927), and 0.834 (95% CI: 0.721-0.947) in the training, test, external validation set1, and external validation set2, respectively. Moreover, the Resnet50 model has great prediction value of prognosis in patients with early-stage OC and OP SCC. The proposed MRI-based Resnet50 DL model demonstrated high capability in diagnosis of OCLNM and prognosis prediction in the early-stage OC and OP SCC. The Resnet50 model could help refine the clinical diagnosis and treatment of the early-stage OC and OP SCC.

Potential value of CT-based comprehensive nomogram in predicting occult lymph node metastasis of esophageal squamous cell paralaryngeal nerves: a two-center study

PurposeThe aim of this study is to construct a combined model that integrates radiomics, clinical risk factors and machine learning algorithms to predict para-laryngeal lymph node metastasis in esophageal squamous cell carcinoma.MethodsA retrospective study included 361 patients with esophageal squamous cell carcinoma from 2 centers. Radiomics features were extracted from the computed tomography scans. Logistic regression, k nearest neighbor, multilayer perceptron, light Gradient Boosting Machine, support vector machine, random forest algorithms were used to construct radiomics models. The receiver operating characteristic curve and The Hosmer–Lemeshow test were employed to select the better-performing model. Clinical risk factors were identified through univariate logistic regression analysis and multivariate logistic regression analysis and utilized to develop a clinical model. A combined model was then created by merging radiomics and clinical risk factors. The performance of the models was evaluated using ROC curve analysis, and the clinical value of the models was assessed using decision curve analysis.ResultsA total of 1024 radiomics features were extracted. Among the radiomics models, the KNN model demonstrated the optimal diagnostic capabilities and accuracy, with an area under the curve (AUC) of 0.84 in the training cohort and 0.62 in the internal test cohort. Furthermore, the combined model exhibited an AUC of 0.97 in the training cohort and 0.86 in the internal test cohort.ConclusionA clinical-radiomics integrated nomogram can predict occult para-laryngeal lymph node metastasis in esophageal squamous cell carcinoma and provide guidance for personalized treatment.

Predicting occult lymph node metastasis in solid-predominantly invasive lung adenocarcinoma across multiple centers using radiomics-deep learning fusion model

BackgroundIn solid-predominantly invasive lung adenocarcinoma (SPILAC), occult lymph node metastasis (OLNM) is pivotal for determining treatment strategies. This study seeks to develop and validate a fusion model combining radiomics and deep learning to predict OLNM preoperatively in SPILAC patients across multiple centers.MethodsIn this study, 1325 cT1a-bN0M0 SPILAC patients from six hospitals were retrospectively analyzed and divided into pathological nodal positive (pN+) and negative (pN-) groups. Three predictive models for OLNM were developed: a radiomics model employing decision trees and support vector machines; a deep learning model using ResNet-18, ResNet-34, ResNet-50, DenseNet-121, and Swin Transformer, initialized randomly or pre-trained on large-scale medical data; and a fusion model integrating both approaches using addition and concatenation techniques. The model performance was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).ResultsAll patients were assigned to four groups: training set (n = 470), internal validation set (n = 202), and independent test set 1 (n = 227) and 2 (n = 426). Among the 1325 patients, 478 (36%) had OLNM (pN+). The fusion model, combining radiomics with pre-trained ResNet-18 features via concatenation, outperformed others with an average AUC (aAUC) of 0.754 across validation and test sets, compared to aAUCs of 0.715 for the radiomics model and 0.676 for the deep learning model.ConclusionThe radiomics-deep learning fusion model showed promising ability to generalize in predicting OLNM from CT scans, potentially aiding personalized treatment for SPILAC patients across multiple centers.

Comparing three-dimensional and two-dimensional deep-learning, radiomics, and fusion models for predicting occult lymph node metastasis in laryngeal squamous cell carcinoma based on CT imaging: a multicentre, retrospective, diagnostic study

The occult lymph node metastasis (LNM) of laryngeal squamous cell carcinoma (LSCC) affects the treatment and prognosis of patients. This study aimed to comprehensively compare the performance of the three-dimensional and two-dimensional deep learning models, radiomics model, and the fusion models for predicting occult LNM in LSCC. In this retrospective diagnostic study, a total of 553 patients with clinical N0 stage LSCC, who underwent surgical treatment without distant metastasis and multiple primary cancers, were consecutively enrolled from four Chinese medical centres between January 01, 2016 and December 30, 2020. The participant data were manually retrieved from medical records, imaging databases, and pathology reports. The study cohort was divided into a training set (n=300), an internal test set (n=89), and two external test sets (n=120 and 44, respectively). The three-dimensional deep learning (3D DL), two-dimensional deep learning (2D DL), and radiomics model were developed using CT images of the primary tumor. The clinical model was constructed based on clinical and radiological features. Two fusion strategies were utilized to develop the fusion model: the feature-based DLRad_FB model and the decision-based DLRad_DB model. The discriminative ability and correlation of 3D DL, 2D DL and radiomics features were analysed comprehensively. The performances of the predictive models were evaluated based on the pathological diagnosis. The 3D DL features had superior discriminative ability and lower internal redundancy compared to 2D DL and radiomics features. The DLRad_DB model achieved the highest AUC (0.89-0.90) among all the study sets, significantly outperforming the clinical model (AUC=0.73-0.78, P=0.0001-0.042, Delong test). Compared to the DLRad_DB model, the AUC values for the DLRad_FB, 3D DL, 2D DL, and radiomics models were 0.82-0.84 (P=0.025-0.46), 0.86-0.89 (P=0.75-0.97), 0.83-0.86 (P=0.029-0.66), and 0.79-0.82 (P=0.0072-0.10), respectively in the study sets. Additionally, the DLRad_DB model exhibited the best sensitivity (82-88%) and specificity (79-85%) in the test sets. The decision-based fusion model DLRad_DB, which combines 3D DL, 2D DL, radiomics, and clinical data, can be utilized to predict occult LNM in LSCC. This has the potential to minimize unnecessary lymph node dissection and prophylactic radiotherapy in patients with cN0 disease. National Natural Science Foundation of China, Natural Science Foundation of Shandong Province.

Blood Markers Predicting Clinically Occult Lymph Node Metastasis in Head and Neck Squamous Cell Carcinoma

Introduction: The presence of cervical lymph node metastases is an unfavorable prognostic factor in head and neck squamous cell carcinoma (HNSCC) and a potential cause of treatment failure. Occult lymph node metastasis occurs in approximately 15–20% of HNSCC patients with a clinically negative neck (cN0), greatly impacting on their prognosis. The present study aimed to investigate the role of pre-treatment peripheral blood markers in predicting clinically occult cervical lymph node metastasis. Methods: This multicenter, retrospective study was performed in a cohort of 472 patients diagnosed with cN0 HNSCC who underwent up-front surgery. Baseline neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic inflammatory marker (SIM), and systemic immune-inflammation index (SII) were calculated from available blood parameters. Results: Oro-hypopharyngeal and oral cancers, locally advanced stage, moderately (G2), and poorly (G3) differentiated grade were associated with an increased risk of pathological lymph node involvement. NLR, LMR, PLR, SIM, and SII were significantly associated at multivariable analysis. NLR >2.12 was the most reliable at predicting occult lymph node metastasis (OR = 5.22; 95% CI: 2.14–12.75). We describe a predictive score integrating cancer site, local stage, and NLR which is effective at predicting positive lymph node pathological status. Conclusions: The present study provides evidence that pre-treatment peripheral blood markers, in particular NLR, represent reliable predictors of clinically occult cervical lymph node metastasis in cN0 HNSCC. Therefore, the present study provides a novel useful predictive score for directing the elective management of the neck in patients with cN0 HNSCC.

Efficacy of radiomics model based on the concept of gross tumor volume and clinical target volume in predicting occult lymph node metastasis in non-small cell lung cancer.

This study aimed to establish a predictive model for occult lymph node metastasis (LNM) in patients with clinical stage I-A non-small cell lung cancer (NSCLC) based on contrast-enhanced CT. A total of 598 patients with stage I-IIA NSCLC from different hospitals were randomized into the training and validation group. The "Radiomics" tool kit of AccuContour software was employed to extract the radiomics features of GTV and CTV from chest-enhanced CT arterial phase pictures. Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was applied to reduce the number of variables and develop GTV, CTV, and GTV+CTV models for predicting occult lymph node metastasis (LNM). Eight optimal radiomics features related to occult LNM were finally identified. The receiver operating characteristic (ROC) curves of the three models showed good predictive effects. The area under the curve (AUC) value of GTV, CTV, and GTV+CTV model in the training group was 0.845, 0.843, and 0.869, respectively. Similarly, the corresponding AUC values in the validation group were 0.821, 0.812, and 0.906. The combined GTV+CTV model exhibited a better predictive performance in the training and validation group by the Delong test (p<0.05). Moreover, the decision curve showed that the combined GTV+CTV predictive model was superior to the GTV or CTV model. The radiomics prediction models based on GTV and CTV can predict occult LNM in patients with clinical stage I-IIA NSCLC preoperatively, and the combined GTV+CTV model is the optimal strategy for clinical application.

18F-FDG PET/CT radiomics nomogram for predicting occult lymph node metastasis of non-small cell lung cancer.

PurposeTo investigate the ability of a PET/CT-based radiomics nomogram to predict occult lymph node metastasis in patients with clinical stage N0 non-small cell lung cancer (NSCLC).Materials and methodsThis retrospective study included 228 patients with surgically confirmed NSCLC (training set, 159 patients; testing set, 69 patients). ITKsnap3.8.0 was used for image(CT and PET images) segmentation, AK version 3.2.0 was used for radiomics feature extraction, and Python3.7.0 was used for radiomics feature screening. A radiomics model for predicting occult lymph node metastasis was established using a logistic regression algorithm. A nomogram was constructed by combining radiomics scores with selected clinical predictors. Receiver operating characteristic (ROC) curves were used to verify the performance of the radiomics model and nomogram in the training and testing sets.ResultsThe radiomics nomogram comprising six selected features achieved good prediction efficiency, including radiomics characteristics and tumor location information (central or peripheral), which demonstrated good calibration and discrimination ability in the training (area under the ROC curve [AUC] = 0.884, 95% confidence interval [CI]: 0.826-0.941) and testing (AUC = 0.881, 95% CI: 0.8031-0.959) sets. Clinical decision curves demonstrated that the nomogram was clinically useful.ConclusionThe PET/CT-based radiomics nomogram is a noninvasive tool for predicting occult lymph node metastasis in NSCLC.

MA03.03 Predicting Occult Lymph Node Metastasis in Patients with Clinical Stage IA Non-small Cell Lung Cancer: A Prospective Cohort Study

Nodules with GGO components have been known as indolent tumors with a low risk of node metastasis, while occult lymph node metastasis (OLNM) is observed in 15-20% even in clinical stage I non-small cell lung cancer (NSCLC) with radiologically solid nodules. We aimed to evaluate predictive factors for occult lymph node metastasis (OLNM) in clinical stage IA NSCLC patients using a prospective observational cohort (OREX-1A: Optimal Extent of Pulmonary Resection in Clinical Stage IA Non-Small Cell Lung Cancer (NCT03066297)).

Deep Learning Analysis Using 18F-FDG PET/CT to Predict Occult Lymph Node Metastasis in Patients With Clinical N0 Lung Adenocarcinoma.

IntroductionThe aim of this work was to determine the feasibility of using a deep learning approach to predict occult lymph node metastasis (OLM) based on preoperative FDG-PET/CT images in patients with clinical node-negative (cN0) lung adenocarcinoma.Materials and MethodsDataset 1 (for training and internal validation) included 376 consecutive patients with cN0 lung adenocarcinoma from our hospital between May 2012 and May 2021. Dataset 2 (for prospective test) used 58 consecutive patients with cN0 lung adenocarcinoma from June 2021 to February 2022 at the same center. Three deep learning models: PET alone, CT alone, and combined model, were developed for the prediction of OLM. The performance of the models was evaluated on internal validation and prospective test in terms of accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curve (AUCs).ResultsThe combined model incorporating PET and CT showed the best performance, achieved an AUC of 0.81 [95% confidence interval (CI): 0.61, 1.00] in the prediction of OLM in internal validation set (n = 60) and an AUC of 0.87 (95% CI: 0.75, 0.99) in the prospective test set (n = 58). The model achieved 87.50% sensitivity, 80.00% specificity, and 81.00% accuracy in the internal validation set and achieved 75.00% sensitivity, 88.46% specificity, and 86.60% accuracy in the prospective test set.ConclusionThis study presented a deep learning approach to enable the prediction of occult nodal involvement based on the PET/CT images before surgery in cN0 lung adenocarcinoma, which would help clinicians select patients who would be suitable for sublobar resection.

Histogram analysis of diffusion-weighted imaging and dynamic contrast-enhanced MRI for predicting occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma.

To investigate the feasibility of whole-tumor histogram analysis of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI for predicting occult lymph node metastasis (LNM) in early-stage oral tongue squamous cell cancer (OTSCC). This retrospective study included 55 early-stage OTSCC (cT1-2N0M0) patients; 34 with pathological LNM and 21 without. Eight whole-tumor histogram features were extracted from quantitative apparent diffusion coefficient (ADC) maps and two semi-quantitative DCE parametric maps (wash-in and wash-out). The clinicopathological factors and histogram features were compared between the two groups. Stepwise logistic regression was used to identify independent predictors. Receiver operating characteristic curves were generated to assess the performances of significant variables and a combined model for predicting occult LNM. MRI-determined depth of invasion and ADCentropy was significantly higher in the LNM group, with respective areas under the curve (AUCs) of 0.67 and 0.69, and accuracies of 0.73 and 0.73. ADC10th. ADCuniformity and wash-inskewness were significantly lower in the LNM group, with respective AUCs of 0.68, 0.71, and 0.69, and accuracies of 0.65, 0.71, and 0.64. Histogram features from wash-out maps were not significantly associated with cervical node status. In the logistic regression analysis, ADC10th, ADCuniformity, and wash-inskewness were independent predictors. The combined model yielded the best predictive performance, with an AUC of 0.87 and an accuracy of 0.82. Whole-tumor histogram analysis of ADC and wash-in maps is a feasible tool for preoperative evaluation of cervical node status in early-stage OTSCC. • Histogram analysis of parametric maps from DWI and DCE-MRI may assist the prediction of occult LNM in early-stage OTSCC. • ADC10th, ADCuniformity, and wash-inskewness were independent predictors. • The combined model exhibited good predictive performance, with an accuracy of 0.82.

Clinicopathological and computed tomographic features associated with occult lymph node metastasis in patients with peripheral solid non-small cell lung cancer

ObjectivesTo investigate the value of combining clinicopathological characteristics with computed tomographic (CT) features of tumours for predicting occult lymph node metastasis (OLNM) in peripheral solid non-small cell lung cancer (PS-NSCLC). MethodsThe study included 478 NSCLC clinically N0 (cN0) patients who underwent lobectomy and systemic lymph node dissection from January 2014 to August 2019. Patients were classified into OLNM and negative lymph node metastasis (NLNM) groups. The CT features of non-metastatic and metastatic lymph nodes with a largest short-diameter > 5 mm were compared in the OLNM group. Thereafter, the clinicopathological characteristics and CT morphological features of tumours were compared between both groups. Multivariable logistic regression analysis and receiver-operating characteristic curve were developed. ResultsCT images detected 103 metastatic and 705 non-metastatic lymph nodes, and no significant differences in CT features of lymph nodes were found in all 161 OLNM patients (P > 0.05). For both groups, sex, carcinoembryonic antigen and pathological type differed significantly (all P < 0.05), while tumour size, necrosis, calcification, vascular convergence, pleural involvement, and the shortest interval of tumour-pleura differed significantly on CT images (all P < 0.05). Multivariable logistic regression analysis showed that carcinoembryonic antigen > 5.00 ng/ml, adenocarcinoma, absence of vascular convergence, and pleural involvement of Type II (one linear or cord-like pleural tag or tumour abut to the pleura with a broad base observed on both lung and mediastinal window images) were independent predicting factors of OLNM. ConclusionsCT findings of lymph nodes can provide limited value and integrating clinicopathological characteristics with the CT morphological features of tumours is helpful in predicting OLNM in patients with PS-NSCLC.

Clinical impact and utility of positron emission tomography on occult lymph node metastasis and survival: radical surgery for stage I lung cancer.

The surgical result of early-staged lung cancer is not satisfactory due to unexpected postoperative lymph node metastasis and recurrence. This study aimed to investigate which preoperative factors-including the standard uptake value max (SUVmax) of positron emission tomography-could predict occult lymph node metastasis and survival. We retrospectively analyzed data from 598 patients with clinical stage I lung cancer who underwent surgery, and examined their preoperative clinical characteristics. A total of 1586 patients had surgery for primary lung cancer between 2006 and 2019; 598 patients with clinical stage I lung cancer were the study inclusion; occult lymph node metastasis was detected in 102 (17.1%). Univariable and multivariable analyses showed that SUVmax ≥ 3 (P < 0.001), clinical invasive tumor size ≥ 2cm (P = 0.009), and carcinoembryonic antigen > 5 (P = 0.03) were associated with significant risk factors rated (%) for occult lymph node metastasis, as follows: high-risk group (three factors), moderate-risk group (two factors) and low-risk group (one factor or none) corresponding to 32.2 (28/87), 22.8 (41/180) and 7.3 (19/262), respectively (P < 0.001). The 5-year overall survival rates (%) of patients without lymph node metastasis holding SUVmax 6 or over were as poor as those of patients with lymph node metastasis (72.0% vs 64.1%; P = 0.56). We might consider wedge resection or segmentectomy, omitting lymphadenectomy, for the low-risk group; adjuvant therapy is indicated for patients without lymph node metastasis having SUVmax 6 or over.

A nomogram for predicting occult lymph node metastasis in early hypopharyngeal cancer with cN0

To explore the risk factors of cervical occult lymph node metastasis (OLNM) in early cN0 hypopharyngeal squamous cell carcinoma (HPSCC), and construct a nomogram model to predict the risk of OLNM in patients with early cN0 HPSCC. 78 cases of early (T1-T2) HPSCC patients who underwent hypopharyngectomy were retrospectively analyzed. Univariate and multivariate logistic regression analyses were used to determine independent risk factors and a nomogram was constructed according to the results of the multivariate logistic regression analysis. Model performance was assessed by constructing a receiver operating characteristic (ROC) curve, and discriminatory capacity assessed using the area under the curve (AUC). Calibration was completed using a plotted calibration curve accompanied by the Hosmer-Lemeshow test. Multivariate logistic regression analysis revealed that age (OR 0.928, 95% CI 0.863-0.997), history of drinking (OR 6.668, 95% CI 1.724-25.788), histological differentiation of tumor (OR 7.269, 95% CI 1.000-52.820), depth of invasion (OR 5.046, 95% CI 1.281-19.874) were independent risk factors of OLNM in early cN0 HPSCC. The ROC curve had an AUC of 0.811 (95% CI 0.713-0.909), which implies good discriminate capacity. The calibration curve and the Hosmer-Lemeshow test (P = 0.972) demonstrated good model fitted and high calibration. A nomogram model based on age, drinking history, histological differentiation of tumor, and depth of tumor invasion was successfully developed to predict occult cervical lymph node metastasis in patients with early cN0 hypopharyngeal cancer.

Machine learning-based MRI texture analysis to predict occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma.

To develop and compare several machine learning models to predict occult cervical lymph node (LN) metastasis in early-stage oral tongue squamous cell cancer (OTSCC) from preoperative MRI texture features. We retrospectively enrolled 116 patients with early-stage OTSCC (cT1-2N0) who had been surgically treated by tumor excision and elective neck dissection (END). For each patient, we extracted 86 texture features from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (ceT1WI), respectively. Dimension reduction was performed in three consecutive steps: reproducibility analysis, collinearity analysis, and information gain algorithm. Models were created using six machine learning methods, including logistic regression (LR), random forest (RF), naïve Bayes (NB), support vector machine (SVM), AdaBoost, and neural network (NN). Their performance was assessed using tenfold cross-validation. Occult LN metastasis was pathologically detected in 42.2% (49/116) of the patients. No significant association was identified between node status and patients' gender, age, or clinical T stage. Dimension reduction steps selected 6 texture features. The NB model gave the best overall performance, which correctly classified the nodal status in 74.1% (86/116) of the carcinomas, with an AUC of 0.802. Machine learning-based MRI texture analysis offers a feasible tool for preoperative prediction of occult cervical node metastasis in early-stage OTSCC. • A machine learning-based MRI texture analysis approach was adopted to predict occult cervical node metastasis in early-stage OTSCC with no evidence of node involvement on conventional images. • Six texture features from T2WI and ceT1WI of preoperative MRI were selected to construct the predictive model. • After comparing six machine learning methods, naïve Bayes (NB) achieved the best performance by correctly identifying the node status in 74.1% of the patients, using tenfold cross-validation.

18F-FDG PET-based radiomics model for predicting occult lymph node metastasis in clinical N0 solid lung adenocarcinoma.

This study aimed to develop a preoperative positron emission tomography (PET)-based radiomics model for predicting occult lymph node metastasis (OLM) in clinical N0 (cN0) solid lung adenocarcinoma. The preoperative fluorine-18-fludeoxyglucose (18F-FDG) PET images of 370 patients with cN0 lung adenocarcinoma confirmed by histopathological examination were retrospectively reviewed. Patients were divided into training and validation sets. Radiomics features and relevant data were extracted from PET images. A nomogram was developed in a training set via univariate and multivariate logistic analyses, and its performance was assessed by concordance-index (C-index), calibration curves, and decision curve analysis (DCA) in the training and validation sets. The multivariate logistic regression analysis showed that only carcinoembryonic antigen (CEA), metabolic tumor volume (MTV), and the radiomics signature had statistically significant differences between patients with and without OLM (P<0.05). A nomogram was developed based on the logistic analyses, and its C-index was 0.769 in the training set and 0.768 in the validation set. The calibration curve demonstrated good consistency between the nomogram-predicted probability of OLM and the actual rate. The DCA also confirmed the clinical utility of the nomogram. A PET/computed tomography (CT)-based radiomics model including CEA, MTV, and the radiomics signature was developed and demonstrated adequate predictive accuracy and clinical net benefit in the present study, and was conveniently used to facilitate the individualized preoperative prediction of OLM.