Evaluation Of Lymph Node Metastasis Research Articles

LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a Probabilistic Lymph Node Atlas

The evaluation of lymph node metastases plays a crucial role in achieving precise cancer staging, which in turn influences subsequent decisions regarding treatment options. The detection of lymph nodes poses challenges due to the presence of unclear boundaries and the diverse range of sizes and morphological characteristics, making it a resource-intensive process. As part of the LNQ 2023 MICCAI challenge, we propose the use of anatomical priors as a tool to address the challenges that persist in automatic mediastinal lymph node segmentation in combination with the partial annotation of the challenge training data. The model ensemble using all suggested modifications yields a Dice score of 0.6033 and segments 57% of the ground truth lymph nodes, compared to 27% when training on CT only. Segmentation accuracy is improved significantly by incorporating a probabilistic lymph node atlas in loss weighting and post-processing. The largest performance gains are achieved by oversampling fully annotated data to account for the partial annotation of the challenge training data, as well as adding additional data augmentation to address the high heterogeneity of the CT images and lymph node appearance. Our code is available at <a href='https://github.com/MICAI-IMI-UzL/LNQ2023'>https://github.com/MICAI-IMI-UzL/LNQ2023</a>.

Magnetic resonance imaging-based lymph node radiomics for predicting the metastasis of evaluable lymph nodes in rectal cancer.

Lymph node (LN) staging in rectal cancer (RC) affects treatment decisions and patient prognosis. For radiologists, the traditional preoperative assessment of LN metastasis (LNM) using magnetic resonance imaging (MRI) poses a challenge. To explore the value of a nomogram model that combines Conventional MRI and radiomics features from the LNs of RC in assessing the preoperative metastasis of evaluable LNs. In this retrospective study, 270 LNs (158 nonmetastatic, 112 metastatic) were randomly split into training (n = 189) and validation sets (n = 81). LNs were classified based on pathology-MRI matching. Conventional MRI features [size, shape, margin, T2-weighted imaging (T2WI) appearance, and CE-T1-weighted imaging (T1WI) enhancement] were evaluated. Three radiomics models used 3D features from T1WI and T2WI images. Additionally, a nomogram model combining conventional MRI and radiomics features was developed. The model used univariate analysis and multivariable logistic regression. Evaluation employed the receiver operating characteristic curve, with DeLong test for comparing diagnostic performance. Nomogram performance was assessed using calibration and decision curve analysis. The nomogram model outperformed conventional MRI and single radiomics models in evaluating LNM. In the training set, the nomogram model achieved an area under the curve (AUC) of 0.92, which was significantly higher than the AUCs of 0.82 (P < 0.001) and 0.89 (P < 0.001) of the conventional MRI and radiomics models, respectively. In the validation set, the nomogram model achieved an AUC of 0.91, significantly surpassing 0.80 (P < 0.001) and 0.86 (P < 0.001), respectively. The nomogram model showed the best performance in predicting metastasis of evaluable LNs.

“Percentage” and “size” of residual viable tumor in lymph node, the performance in estimating pathologic response of lymph node in non-small cell lung cancer treated with neoadjuvant chemoimmunotherapy

There is no universally accepted method for evaluating lymph node metastasis (LNM) in non-small cell lung cancer (NSCLC) after neoadjuvant chemoimmunotherapy. Different protocols recommend evaluating the percentage of residual viable tumor (RVT%) and metastatic tumor size (MTS). Our aim was to determine the prognostic significance of RVT% and MTS, and identify the more effective parameter for pathological evaluating LNM. Two independent cohorts were collected (derivation, n=84; external validation, n=42). All patients exhibited metastatic cancer or treatment response in lymph nodes post-surgery. In the derivation cohort, we assessed the mean and largest values of MTS and RVT% in LNM, estimating their optimal cutoffs for event-free survival (EFS) using maximally selected rank statistics. Validation was subsequently conducted in the external validation cohort. The quality of prognostic factors was evaluated using the Area Under Curve (AUC). A positive association was identified between RVT% and MTS, but an absolute association could not be conclusively established. In the derivation cohort, neither the largest MTS (cutoff=6mm, p=0.28), largest RVT% (cutoff=75%, p=0.23), nor mean RVT% (cutoff=55%, p=0.06) were associated with EFS. However, mean MTS (cutoff=4.5mm) in lymph nodes was statistically associated with EFS (p=0.018), validated by the external cohort (p=0.017). The prognostic value of MTS exceeded that of ypN staging in both cohorts, as evidenced by higher AUC values. The mean value of MTS can effectively serve as a parameter for the pathological evaluation of lymph nodes, with a threshold of 4.5mm, closely linked to EFS. Its prognostic value outperforms that of ypN staging.

Multiparametric evaluation of mediastinal lymph node metastases in clinical T0-T1c stage non-small-cell lung cancers.

This study aimed to determine the predictive factors of lymph node metastases in clinical T0-T1c stage non-small-cell lung cancers, so as to help making surgical strategy. From January 2016 to December 2017, patients with clinical T0-T1c stage non-small-cell lung cancers were retrospectively reviewed. We elucidated the lymph node metastatic incidence and distribution according to the primary tumour radiographic findings and maximal standard uptake values, and extracted the associated clinicopathological factors. Univariable and multivariable logistic regressions were used to identify independent predictive parameters for lymph node metastases. The performance of predictive model was evaluated using receiver operating characteristic analysis. A total of 517 patients were included. Seventy-two patients had lymph node metastases. Among patients with pure ground-glass nodule and solid component size ≤10 mm, none had any lymph node metastasis. Multivariable logistic regression analysis demonstrated that age, carcinoembryonic antigen level, solid component size, consolidation-tumour ratio and tumour maximal standard uptake values were independent predictors of lymph nodal metastases. Receiver operating characteristic analyses indicated that the area under the curve of predictive model in evaluating lymph node metastases was 0.838 (95% CI 0.791-0.886). Younger age, elevated carcinoembryonic antigen level, larger solid component size, higher consolidation-tumour ratio and tumour maximal standard uptake values were associated with lymph node involvement. Employing such a predictive model in the future may affect the surgical option of lymph node excision for patients in cT1 stage non-small-cell lung cancer.

Risk Factor Analysis of Lymph Node Metastasis for Rectal Neuroendocrine Tumors: Who Needs a Radical Resection in Rectal Neuroendocrine Tumors Sized 1-2 cm?

Rectal neuroendocrine tumors (NETs) have malignant potential, and lymph node (LN) or distant metastases can occur; however, treatment of NETs 1-2cm in size is controversial. This study aimed to identify predictive factors for LN metastasis and prognostic factors for recurrence of rectal NETs, especially tumors 1‒2cm in size. Between October 2004 and November 2020, 453 patients underwent endoscopic or surgical treatment for rectal NETs in Seoul National University Hospital. The data on these patients were prospectively collected in our database and reviewed retrospectively. In cases of local excision, we evaluated LN metastasis with radiologic imaging, including computed tomography or magnetic resonance imaging before treatment and during the follow-up periods. LN metastasis was observed in 40 patients (8.8%). A higher rate of LN metastasis was observed in larger-sized tumors, advanced T stage, lymphovascular invasion (LVI), perineural invasion (PNI), and high tumor grade. In multivariable analysis, the significant risk factors for LN metastasis were tumor size (1≤ size < 2 cm: hazard ratio [HR] 64.07; size ≥2 cm: HR 102.37, p<0.001) and tumor grade (G2: HR 3.63, p=0.034; G3: HR 5.09, p=0.044). In multivariable analysis for tumors 1-2cm in size, the risk factor for LN metastasis was tumor grade (G2: HR 6.34, p=0.013). Tumor grade and size are important predictive factors for LN metastasis. In NETs 2cm in size, tumor grade is also important for LN metastasis, and radical resection should be considered.

Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer.

To investigate the value of a clinical-MRI radiomics model based on clinical characteristics and T2-weighted imaging (T2WI) for preoperatively evaluating lymph node (LN) metastasis in patients with MRI-predicted low tumor (T) staging rectal cancer (mrT1, mrT2, and mrT3a with extramural spread ≤ 5mm). This retrospective study enrolled 303 patients with low T-staging rectal cancer (training cohort, n = 213, testing cohort n = 90). A total of 960 radiomics features were extracted from T2WI. Minimum redundancy and maximum relevance (mRMR) and support vector machine were performed to select the best performed radiomics features for predicting LN metastasis. Multivariate logistic regression analysis was then used to construct the clinical and clinical-radiomics combined models. The model performance for predicting LN metastasis was assessed by receiver operator characteristic curve (ROC) and clinical utility implementing a nomogram and decision curve analysis (DCA). The predictive performance for LN metastasis was also compared between the combined model and human readers (2 seniors). Fourteen radiomics features and 2 clinical characteristics were selected for predicting LN metastasis. In the testing cohort, a higher positive predictive value of 75.9% for the combined model was achieved than those of the clinical model (44.8%) and two readers (reader 1: 54.9%, reader 2: 56.3%) in identifying LN metastasis. The interobserver agreement between 2 readers was moderate with a kappa value of 0.416.A clinical-radiomics nomogram and decision curve analysis demonstrated that the combined model was clinically useful. T2WI-based radiomics combined with clinical data could improve the efficacy in noninvasively evaluating LN metastasis for the low T-staging rectal cancer and aid in tailoring treatment strategies.

An Updated Review on Imaging and Staging of Anal Cancer-Not Just Rectal Cancer.

This review discusses the histology, MR technique, diagnosis, staging, and treatment of anal cancer, with a particular focus on the differences in TNM staging between anal squamous-cell carcinoma (ASCC) and rectal adenocarcinoma. To conduct this updated review, a comprehensive literature search was performed using prominent medical databases, including PubMed and Embase. The search was limited to articles published within the last 10 years (2013-2023) to ensure their relevance to the current state of knowledge. (1) articles that provided substantial information on the diagnostic techniques used for ASCC, mainly focusing on imaging, were included; (2) studies reporting on emerging technologies; (3) English-language articles. articles that did not meet the inclusion criteria, case reports, or articles with insufficient data. The primary outcome of this review is to assess the accuracy and efficacy of different diagnostic modalities, including CT, MRI, and PET, in diagnosing ASCC. The secondary outcomes are as follows: (1) to identify any advancements or innovations in diagnostic techniques for ASCC over the past decade; (2) to highlight the challenges and limitations of the diagnostic process. ASCC is a rare disease; however, its incidence has been steadily increasing. Primary staging and assessment after chemoradiation therapy are commonly performed using MRI, which is considered to be the preferred imaging modality. CT and PET/CT are useful in evaluating lymph node metastases and distant metastatic disease. ASCC and rectal adenocarcinoma are the most common histological subtypes and are typically indistinguishable on MRI; therefore, a biopsy prior to imaging is necessary to stage the tumor accurately and determine the treatment approach.

Qualitative and quantitative PET/MRI in the evaluation of axillary lymph node metastasis in patients with breast cancer

Objective: To investigate the value of qualitative and quantitative PET/MRI in the evaluation of axillary lymph node metastasis in patients with breast cancer. Methods: A total of 33 patients with breast cancer underwent preoperative PET/MRI examinations in Jingling Hospital from February to August in 2022 were retrospectively collected. All these patients were female, aged from 34 to 73 (51.4±11.3) years. Histopathological results and follow-up data were deemed as the referent standard, and the images were independently evaluated by two experienced breast imaging radiologists. The qualitative PET/MRI evaluation procedures were designed to evaluated the MRI alone to classify the axillary lymph nodes firstly, and then, the axillary lymph nodes status was reclassified by combining MRI and PET images. The net reclassification improvement index (NRI) was calculated using the R Programming Language (RStudio). The quantitative PET/MRI evaluation of the maximum standard uptake value (SUVmax) of axillary lymph nodes were measured by two radiologists, respectively, and the average value was compared with the referent standard to conduct a receiver operating characteristic (ROC) curve to select the optimal cutoff value of SUVmax. Based on the cutoff value and MRI classification results, axillary lymph nodes status was divided into quantitative PET/MRI positive or negative. The sensitivity, specificity and accuracy of MRI and quantitative PET/MRI in evaluating axillary lymph node metastasis were compared, and the area under the ROC curve (AUC) was compared. Results: There was no significant difference in sensitivity, specificity and accuracy between MRI and quantitative PET/MRI in evaluating lymph node metastasis of breast cancer (81.82% vs 95.46%; 81.82% vs 100%; 81.82% vs 96.97%) (all P>0.05). The AUC had a statistically significant difference [0.82 (0.65 to 0.93) vs 0.98 (0.85 to 1.00), P=0.026)]. According to the referent standard, in the 11 cases without ipsilateral axillary lymph node metastasis, the SUVmax was 0.83±0.18, while in the 22 cases with ipsilateral axillary lymph node metastasis, the SUVmax was [4.36 (1.77, 5.85)]. Compared with MRI alone, the NRI of qualitative PET/MRI in evaluating lymph node metastasis was 36.36% (P=0.021). Conclusion: Compared with MRI alone, quantitative PET/MRI has a higher AUC for evaluating axillary lymph node metastasis in patients with breast cancer, and qualitative PET/MRI had a better reclassification power in the evaluation of axillary lymph node metastasis.

Identification of lymph node metastasis in pre-operation cervical cancer patients by weakly supervised deep learning from histopathological whole-slide biopsy images.

Lymph node metastasis (LNM) significantly impacts the prognosis of individuals diagnosed with cervical cancer, as it is closely linked to disease recurrence and mortality, thereby impacting therapeutic schedule choices for patients. However, accurately predicting LNM prior to treatment remains challenging. Consequently, this study seeks to utilize digital pathological features extracted from histopathological slides of primary cervical cancer patients to preoperatively predict the presence of LNM. A deep learning (DL) model was trained using the Vision transformer (ViT) and recurrent neural network (RNN) frameworks to predict LNM. This prediction was based on the analysis of 554 histopathological whole-slide images (WSIs) obtained from Qilu Hospital of Shandong University. To validate the model's performance, an external test was conducted using 336 WSIs from four other hospitals. Additionally, the efficiency of the DL model was evaluated using 190 cervical biopsies WSIs in a prospective set. In the internal test set, our DL model achieved an area under the curve (AUC) of 0.919, with sensitivity and specificity values of 0.923 and 0.905, respectively, and an accuracy (ACC) of 0.909. The performance of the DL model remained strong in the external test set. In the prospective cohort, the AUC was 0.91, and the ACC was 0.895. Additionally, the DL model exhibited higher accuracy compared to imaging examination in the evaluation of LNM. By utilizing the transformer visualization method, we generated a heatmap that illustrates the local pathological features in primary lesions relevant to LNM. DL-based image analysis has demonstrated efficiency in predicting LNM in early operable cervical cancer through the utilization of biopsies WSI. This approach has the potential to enhance therapeutic decision-making for patients diagnosed with cervical cancer.

Prediction of lymph node metastasis of well-differentiated rectal neuroendocrine tumours using multiple diagnostic modalities.

The preoperative prediction of lymph node metastasis of well-differentiated rectal neuroendocrine tumours is highly desirable and useful in defining surgical indication more accurately. We aimed to evaluate lymph node metastasis in rectal neuroendocrine neoplasms using multiple imaging modalities. The clinical records and radiological images of 70 patients with well-differentiated rectal neuroendocrine tumours who received treatment at the University of Tokyo Hospital between 2010 and 2022 were retrospectively analysed. The relationship between evaluation by multiple imaging modalities and pathological lymph node metastasis was analysed. The receiver operating characteristic curves showed that a maximum lymph node diameter ≥4 mm on computed tomography and ≥8 mm on magnetic resonance imaging were the optimal predictive factors for lymph node metastasis. Accumulation in the lymph nodes on somatostatin receptor scintigraphy (P = 0.058) and Delle's findings on colonoscopy (P = 0.014) were also significant predictors of pathological lymph node positivity, and combination of multiple modalities was useful. Pathologically, lymphatic (P = 0.0030)/venous (P = 0.0007) invasion were risk factors for lymph node metastasis. In addition to pathological risk factors, a combination of multiple radiological imaging modalities is useful for predicting lymph node metastasis in well-differentiated rectal neuroendocrine tumours.

A convolutional neural network with self-attention for fully automated metabolic tumor volume delineation of head and neck cancer in [^{18}F]FDG PET/CT

PurposePET-derived metabolic tumor volume (MTV) and total lesion glycolysis of the primary tumor are known to be prognostic of clinical outcome in head and neck cancer (HNC). Including evaluation of lymph node metastases can further increase the prognostic value of PET but accurate manual delineation and classification of all lesions is time-consuming and prone to interobserver variability. Our goal, therefore, was development and evaluation of an automated tool for MTV delineation/classification of primary tumor and lymph node metastases in PET/CT investigations of HNC patients.MethodsAutomated lesion delineation was performed with a residual 3D U-Net convolutional neural network (CNN) incorporating a multi-head self-attention block. 698 [^{18}F]FDG PET/CT scans from 3 different sites and 5 public databases were used for network training and testing. An external dataset of 181 [^{18}F]FDG PET/CT scans from 2 additional sites was employed to assess the generalizability of the network. In these data, primary tumor and lymph node (LN) metastases were interactively delineated and labeled by two experienced physicians. Performance of the trained network models was assessed by 5-fold cross-validation in the main dataset and by pooling results from the 5 developed models in the external dataset. The Dice similarity coefficient (DSC) for individual delineation tasks and the primary tumor/metastasis classification accuracy were used as evaluation metrics. Additionally, a survival analysis using univariate Cox regression was performed comparing achieved group separation for manual and automated delineation, respectively.ResultsIn the cross-validation experiment, delineation of all malignant lesions with the trained U-Net models achieves DSC of 0.885, 0.805, and 0.870 for primary tumor, LN metastases, and the union of both, respectively. In external testing, the DSC reaches 0.850, 0.724, and 0.823 for primary tumor, LN metastases, and the union of both, respectively. The voxel classification accuracy was 98.0% and 97.9% in cross-validation and external data, respectively. Univariate Cox analysis in the cross-validation and the external testing reveals that manually and automatically derived total MTVs are both highly prognostic with respect to overall survival, yielding essentially identical hazard ratios (HR) (text {HR}_{text {man}}=1.9; p<0.001 vs. text {HR}_{text {cnn}}=1.8; p<0.001 in cross-validation and text {HR}_{text {man}}=1.8; p=0.011 vs. text {HR}_{text {cnn}}=1.9; p=0.004 in external testing).ConclusionTo the best of our knowledge, this work presents the first CNN model for successful MTV delineation and lesion classification in HNC. In the vast majority of patients, the network performs satisfactory delineation and classification of primary tumor and lymph node metastases and only rarely requires more than minimal manual correction. It is thus able to massively facilitate study data evaluation in large patient groups and also does have clear potential for supervised clinical application.

Characteristics of systematic lymph node dissection and influencing factors of sentinel lymph node biopsy using carbon nanoparticles in endometrial carcinoma: a single-center study

BackgroundCarbon nanoparticles (CNPs) are a new tracer for lymph node mapping, which can quickly reach and develop lymph nodes through a lymphatic network. This research investigated the characteristics of systematic lymph node dissection and sentinel lymph node biopsy mapped with CNPs in endometrial carcinoma.MethodsWe first applied CNPs to systematic lymph node dissection in 18 endometrial carcinoma patients as the study group and another 18 endometrial carcinoma patients who were not injected with anything served as the control group. Then, we applied CNPs to sentinel lymph nodes biopsy in 54 endometrial carcinoma patients. All 54 patients received systematic lymph node dissection after sentinel lymph node biopsy. The detection rate, sensitivity, specificity, and accuracy of systematic lymph node dissection and sentinel lymph node biopsy by CNPs were respectively analyzed. A nomogram model for predicting the success of sentinel lymph node mapping was established.ResultsThe average number of lymph nodes removed in the CNP-labeled study group was higher than that in the control group (p<0.001). CNPs improved the number of lymph nodes with a diameter ≤ 0.5cm. The detection rate, sensitivity, specificity, and accuracy of sentinel lymph nodes biopsy by CNPs for endometrial carcinoma were 70.4%, 100%, 100%, and 100%, respectively. The nomogram model included factors of long menopause time, cervical cyst, and hard cervical texture, and the area of ROC curve was 0.816.ConclusionsCNPs improve the detection rate of small lymph nodes. CNPs can trace sentinel lymph nodes in evaluating lymph node metastasis in endometrial carcinoma.

Impact of near-infrared fluorescence imaging with indocyanine green on the surgical treatment of pulmonary masses in dogs.

To investigate the intraoperative identification and complete resection of pulmonary masses, and to evaluate lymph node metastasis of pulmonary malignant tumors in dogs using indocyanine green (ICG) fluorescence imaging. Forty dogs with pulmonary masses were included, all of which underwent surgical treatment. ICG fluorescence imaging was performed on pulmonary masses before lobectomy and the resection margins after lobectomy. In addition, ICG fluorescence of the excised masses and lymph nodes was evaluated in the shaded box. The fluorescence findings were compared with the histopathological diagnosis. Of 44 nodules resected from 40 dogs, 32 nodules were histopathologically diagnosed as lung adenocarcinoma, five were histiocytic sarcoma, three were undifferentiated sarcoma, two were malignant epithelial tumor metastases, one was carcinosarcoma, and one was a non-neoplastic lesion. Fluorescence was observed in all nodules. In addition to the main lesion, other fluorescent nodules were found in four dogs. Regarding the diagnostic accuracy of complete resection based on ICG fluorescence, the sensitivity was 67.7% and the specificity was 60.0%. The sensitivity and specificity of ICG fluorescence for the diagnosis of lymph node metastasis were 100 and 75.0%, respectively. ICG fluorescence imaging might be a useful intraoperative diagnostic method to identify the location of tumors and lymph node metastasis, but not to evaluate complete tumor resection, in dogs with pulmonary malignant tumors.

REMARK scoring of biomarkers predicting lymph node metastasis in oral squamous cell carcinoma – A systematic review

Background: A universal and systematic protocol is essential for accurate reporting of biomarker studies. For unity in reporting biomarker studies, many guidelines were introduced, Recommendations for Tumor Marker Prognostic Studies (REMARK) being one of them. Aim: The purpose of this review is to evaluate the quality of published articles of biomarkers that predict metastasis in lymph nodes in oral squamous cell carcinoma (OSCC) by the use of the reporting recommendations for tumor marker prognostic (REMARK) guidelines. Methods: Comprehensive search was done in MEDLINE via PubMed and Cochrane from January 2015 to December 2019 to identify manuscripts evaluating biomarkers predicting lymph node metastasis in OSCC. The significance of the univariate and multivariate analysis was assessed for each manuscript, and P < 0.05 was considered statistically significant. Results: Thirty-six results were included for the qualitative synthesis. The mean REMARK score was 11.13 (range: 5.01–17.15). Biomarkers with the highest REMARK score were phospholipase C, cyclin D, CD44+/CD133+, and matrix metalloproteinase-9 (MMP-9). While biomarkers such as LGALS1, NCOA7, and TMOD1 were associated with high risk of bias, hence its use as a biomarker predicting lymph node metastasis is questionable. Conclusions: In our review of 36 manuscripts, manuscripts examining biomarkers evaluating lymph node metastasis in OSCC need an improvement in their reporting. Biomarkers such as phospholipase C, cyclin D, CD44+/CD133+, and MMP-9 can be used as a predictor of lymph node metastasis in OSCC.

A new radiomics approach combining the tumor and peri-tumor regions to predict lymph node metastasis and prognosis in gastric cancer.

The development of non-invasive methods for evaluating lymph node metastasis (LNM) preoperatively in gastric cancer (GC) is necessary. In this study, we developed a new radiomics model combining features from the tumor and peri-tumor regions for predicting LNM and prognoses. This was a retrospective observational study. In this study, two cohorts of patients with GC treated in Zhongshan Hospital Fudan University (Shanghai, China) were included. In total, 193 patients were assigned to the internal training/validation cohort; another 98 patients were assigned to the independent testing cohort. The radiomics features were extracted from venous phase computerized tomography (CT) images. The radiomics model was constructed and the output was defined as the radiomics score (RS). The performance of the RS and CT-defined N status (ctN) for predicting LNM was compared using the area under the curve (AUC). The 5-year overall survival and progression-free survival were compared between different subgroups using Kaplan-Meier curves. In both cohorts, the RS was significantly higher in the LNM-positive group than that in the LNM-negative group (all P < 0.001). The radiomics model combining features from the tumor and peri-tumor regions achieved the highest AUC in predicting LNM (AUC, 0.779 and 0.724, respectively), which performed better than the radiomics model based only on the tumor region and ctN (AUC, 0.717, 0.622 and 0.710, 0.603, respectively). The differences in 5-year overall survival and progression-free survival between high-risk and low-risk groups were significant (both P < 0.001). The radiomics model combining features from the tumor and peri-tumor regions could effectively predict the LNM in GC. Risk stratification based on the RS was capable of distinguishing patients with poor prognoses.

A nomogram model based on MRI and radiomic features developed and validated for the evaluation of lymph node metastasis in patients with rectal cancer.

The aim of this study was to develop and validate a nomogram model to evaluate lymph node metastasis (LNM) in patients with rectal cancer (RC). A total of 162 patients with RC were included in the study. The MRIreported model, the Radscore model, and the Complex model were constructed using the logistics regression (LR) algorithm. The DeLong test and decision curve analysis (DCA) were used to compare the prediction performance and clinical utility of these models. The nomogram model was constructed to visualize the prediction results of the best model. Model performance was evaluated in the training and validation groups, and the calibration curve and Hosmer-Lemeshow goodness of fit test were used to evaluate the calibration. All three models constructed by the LR algorithm were good at identifying LNM. The DeLong test and the DCA results showed that the Complex model outperformed the MRIreported model and the Radscore model in relation to their predictive performance and clinical utility. The nomogram of the Complex model had an area under the curve (AUC) of 0.902 (95% confidence interval (CI) 0.848-0.957) in the training group and an AUC of 0.891 (95% CI 0.799-0.983) in the validation group. Meanwhile, the nomogram showed good calibration. The nomogram model constructed based on T2WI radiomics and MRI reported had good diagnostic efficacies for LNM in patients with RC, and provided a new auxiliary method for accurate and individualized clinical management.

Computed Tomography-Based Deep Learning Nomogram Can Accurately Predict Lymph Node Metastasis in Gastric Cancer

BackgroundComputed tomography is the most commonly used imaging modality for preoperative assessment of lymph node status, but the reported accuracy is unsatisfactory.AimsTo evaluate and verify the predictive performance of computed tomography deep learning on the presurgical evaluation of lymph node metastasis in patients with gastric cancer.Methods347 patients were retrospectively selected (training cohort: 242, test cohort: 105). The enhanced computed tomography arterial phase images of gastric cancer were used for lesion segmentation, radiomics and deep learning feature extraction. Three methods were used for feature selection. Support vector machine (SVM) or random forest (RF) was used to build models. The classification performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC). We also established a nomogram that included clinical predictors.ResultsThe model based on ResNet50-RF showed favorable classification performance and was verified in the test cohort (AUC = 0.9803). The nomogram based on deep learning feature scores and the lymph node status reported by computed tomography showed excellent discrimination. AUC of 0.9978 was achieved in the training cohort and verified in the test cohort (AUC = 0.9914). Decision analysis curve showed the value of nomogram in clinical application.ConclusionThe computed tomography-based deep learning nomogram can accurately and effectively evaluate lymph node metastasis in patients with gastric cancer before surgery.

Imaging Assessment of Interval Metastasis from Melanoma.

Interval metastasis is a particular metastatic category of metastatic localizations in the lymph nodes in patients with melanoma. Interval nodes are generally located at nonregional lymphatic stations placed along the pathway of the spread of melanoma, such as the epitrochlear lymph node station, the popliteal fossa, and the retroareolar station. Imaging techniques for evaluation of patients with interval metastasis from melanoma diseases include ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), lymphoscintigraphy (LS), and positron emission tomography (PET). A literature review was conducted through a methodical search on the Pubmed and Embase databases. The evaluation of lymph node metastases represents a critical phase in the staging and follow-up of melanoma patients. Therefore, a thorough knowledge of the imaging methods available and the interactions between the clinician and the radiologist are essential for making the correct choice for individual patients, for a better management, and to improve treatment and survival.

Validation of whole slide imaging for frozen section diagnosis of lymph node metastasis: A retrospective study from a tertiary care hospital in Thailand

BackgroundThe use of whole slide imaging (WSI) for frozen section (FS) diagnosis is helpful, particularly in the context of pathologist shortages. However, there is minimal data on such usage in resource-limited settings. This study aims to validate the use of WSI for FS diagnosis of lymph node metastasis using a low-cost virtual microscope scanner with consumer-grade laptops at a tertiary care hospital in Thailand. MethodsFS slides were retrieved for which the clinical query was to evaluate lymph node metastasis. They were digitized by a virtual microscope scanner (MoticEasyScan, Hong Kong) using up to 40× optical magnification. Three observers with different pathology experience levels diagnosed each slide, reviewing glass slides (GS) followed by digital slides (DS) after two weeks of a wash out period. WSI and GS diagnoses were compared. The time used for scanning and diagnosis of each slide was recorded. Results295 FS slides were retrieved and digitized. The first-time successful scanning rate was 93.6 %. The mean scanning time was 2 min per slide. Both intraobserver agreement and interobserver agreement of WSI and GS diagnoses were high (Cohen's K; kappa value >0.84). The time used for DS diagnosis decreased as the observer's experience with WSI increased. ConclusionsDespite varying pathological experiences, observers using WSI provided accurate FS diagnoses of lymph node metastasis. The time required for DS diagnoses decreased with additional observer's experience with WSI. Therefore, a WSI system containing low-cost scanners and consumer-grade laptops could be used for FS services in hospital laboratories lacking pathologists.

A Dual-Energy CT Radiomics of the Regional Largest Short-Axis Lymph Node Can Improve the Prediction of Lymph Node Metastasis in Patients With Rectal Cancer.

ObjectiveTo explore the value of dual-energy computed tomography (DECT) radiomics of the regional largest short-axis lymph nodes for evaluating lymph node metastasis in patients with rectal cancer.Materials and MethodsOne hundred forty-one patients with rectal cancer (58 in LNM+ group, 83 in LNM- group) who underwent preoperative total abdominal DECT were divided into a training group and testing group (7:3 ratio). After post-processing DECT venous phase images, 120kVp-like images and iodine (water) images were obtained. The highest-risk lymph nodes were identified, and their long-axis and short-axis diameter and DECT quantitative parameters were measured manually by two experienced radiologists who were blind to the postoperative pathological results. Four DECT parameters were analyzed: arterial phase (AP) normalized iodine concentration, AP normalized effective atomic number, the venous phase (VP) normalized iodine concentration, and the venous phase normalized effective atomic number. The carcinoembryonic antigen (CEA) levels were recorded one week before surgery. Radiomics features of the largest lymph nodes were extracted, standardized, and reduced before modeling. Radomics signatures of 120kVp-like images (Rad-signature120kVp) and iodine map (Rad-signatureImap) were built based on Logistic Regression via Least Absolute Shrinkage and Selection Operator (LASSO).ResultsEight hundred thirty-three features were extracted from 120kVp-like and iodine images, respectively. In testing group, the radiomics features based on 120kVp-like images showed the best diagnostic performance (AUC=0.922) compared to other predictors [CT morphological indicators (short-axis diameter (AUC=0.779, IDI=0.262) and long-axis diameter alone (AUC=0.714, IDI=0.329)), CEA alone (AUC=0.540, IDI=0.414), and normalized DECT parameters alone (AUC=0.504-0.718, IDI=0.290-0.476)](P<0.05 in Delong test). Contrary, DECT iodine map-based radiomic signatures showed similar performance in predicting lymph node metastasis (AUC=0.866). The decision curve showed that the 120kVp-like-based radiomics signature has the highest net income.ConclusionPredictive model based on DECT and the largest short-axis diameter lymph nodes has the highest diagnostic value in predicting lymph node metastasis in patients with rectal cancer.