Diagnosis Of Lymph Node Metastasis Research Articles

Diagnostic accuracy of radiomics and artificial intelligence models in diagnosing lymph node metastasis in head and neck cancers: a systematic review and meta-analysis.

Head and neck cancers are the seventh most common globally, with lymph node metastasis (LNM) being a critical prognostic factor, significantly reducing survival rates. Traditional imaging methods have limitations in accurately diagnosing LNM. This meta-analysis aims to estimate the diagnostic accuracy of Artificial Intelligence (AI) models in detecting LNM in head and neck cancers. A systematic search was performed on four databases, looking for studies reporting the diagnostic accuracy of AI models in detecting LNM in head and neck cancers. Methodological quality was assessed using the METRICS tool and meta-analysis was performed using bivariate model in R environment. 23 articles met the inclusion criteria. Due to the absence of external validation in most studies, all analyses were confined to internal validation sets. The meta-analysis revealed a pooled AUC of 91% for CT-based radiomics, 84% for MRI-based radiomics, and 92% for PET/CT-based radiomics. Sensitivity and specificity were highest for PET/CT-based models. The pooled AUC was 92% for deep learning models and 91% for hand-crafted radiomics models. Models based on lymph node features had a pooled AUC of 92%, while those based on primary tumor features had an AUC of 89%. No significant differences were found between deep learning and hand-crafted radiomics models or between lymph node and primary tumor feature-based models. Radiomics and deep learning models exhibit promising accuracy in diagnosing LNM in head and neck cancers, particularly with PET/CT. Future research should prioritize multicenter studies with external validation to confirm these results and enhance clinical applicability.

Utilizing the Postvascular Phase of Contrast-Enhanced Ultrasound to Predict Breast Cancer Lymph Node Metastasis

Background and Objectives: To evaluate the value of the postvascular phase of contrast-enhanced ultrasound (CEUS) in differentiating between benign and metastatic lymph nodes (LNs) in patients with breast cancer (BC). Materials and Methods: This study retrospectively analyzed 96 suspicious LNs in the lymphatic drainage area of the breast from 90 patients with BC. All LNs were assessed by conventional ultrasound (US) and CEUS following intravenous Sonazoid injection. All LNs underwent puncture biopsy, and pathological results were obtained. The correlations between US and CEUS indicators of LNs and LN metastasis (LNM) were analyzed. Results: Of the 96 LNs, 66 were metastatic. Overall, 80.00% (24/30) of the benign LNs exhibited relative hyper-enhancement in the postvascular phase, whereas 96.97% (64/66) of the metastatic LNs exhibited relative hypo-enhancement (p < 0.001). This CEUS finding was highly predictive of metastasis, with a sensitivity of 96.97%, specificity of 80.00%, positive predictive value of 91.43%, negative predictive value of 92.31%, and accuracy of 91.67%. The mean postvascular phase intensity (MPI) was significantly lower for malignant (median MPI, 12 dB) than for benign (median MPI, 75 dB) LNs. The postvascular phase was more sensitive, specific, and accurate than conventional US or the vascular phase of CEUS for the diagnosis of LNM, with an area under the curve of 0.95 (95% confidence interval: 0.89–0.99). Conclusions: Qualitative and quantitative indicators of the postvascular phase of CEUS provide a reliable diagnostic approach to differentiate benign and metastatic LNs in patients with BC.

Lymph node metastasis of intrahepatic cholangiocarcinoma: the present and prospect of detection and dissection.

Intrahepatic cholangiocarcinoma (ICC) ranks as the second most primary liver cancer that often goes unnoticed with a high mortality rate. Hepatectomy is the main treatment for ICC, but only 15% of patients are suitable for surgery. Despite advancements in therapeutic approaches, ICC has an unfavorable prognosis, largely due to lymph node metastasis (LNM) that is closely linked to the elevated recurrence rates. Consequently, the identification of precise and suitable techniques for the detection and staging of LNM assumes paramount importance for ICC therapy. While preoperative imaging plays a crucial role in ICC diagnosis, its efficacy in accurately diagnosing LNM remains unsatisfactory. The inclusion of lymph node dissection as part of the hepatectomy procedures is significant for the accurate pathological diagnosis of LNM, although it continues to be a topic of debate. The concept of sentinel lymph node in ICC has presented a novel and potentially valuable approach for diagnosing LNM. This review aims to explore the current state and prospects of LNM in ICC, offering a promising avenue for enhancing the clinical diagnosis and treatment of ICC to improve patient prognosis.

Automatic tumor segmentation and lymph node metastasis prediction in papillary thyroid carcinoma using ultrasound keyframes.

Accurate preoperative prediction of cervical lymph node metastasis (LNM) for papillary thyroid carcinoma (PTC) patients is essential for disease staging and individualized treatment planning, which can improve prognosis and facilitate better management. To establish a fully automated deep learning-enabled model (FADLM) for automated tumor segmentation and cervical LNM prediction in PTC using ultrasound (US) video keyframes. The bicentral study retrospective enrolled 518 PTC patients, who were then randomly divided into the training (Hospital 1, n=340), internal test (Hospital 1, n=83), and external test cohorts (Hospital 2, n=95). The FADLM integrated mask region-based convolutional neural network (Mask R-CNN) for automatic thyroid primary tumor segmentation and ResNet34 with Bayes strategy for cervical LNM diagnosis. A radiomics model (RM) using the same automated segmentation method, a traditional radiomics model (TRM) using manual segmentation, and a clinical-semantic model (CSM) were developed for comparison. The dice similarity coefficient (DSC) was used to evaluate segmentation performance. The prediction performance of the models was validated in terms of discrimination and clinical utility with the area under the receiver operator characteristic curve (AUC), heatmap analysis, and decision curve analysis (DCA). The comparison of the predictive performance among different models was conducted by DeLong test. The performances of two radiologists compared with FADLM and the diagnostic augmentation with FADLM's assistance were analyzed in terms of accuracy, sensitivity and specificity using McNemar's x2 test. The p-value less than 0.05 was defined as a statistically significant difference. The Benjamini-Hochberg procedure was applied for multiple comparisons to deal with Type I error. The FADLM yielded promising segmentation results in training (DSC: 0.88±0.23), internal test (DSC: 0.88±0.23), and external test cohorts (DSC: 0.85±0.24). The AUCs of FADLM for cervical LNM prediction were 0.78 (95% CI: 0.73, 0.83), 0.83 (95% CI: 0.74, 0.92), and 0.83 (95% CI: 0.75, 0.92), respectively. It all significantly outperformed the RM (AUCs: 0.78vs. 0.72; 0.83vs. 0.65; 0.83vs. 0.68, all adjusted p-values<0.05) and CSM (AUCs: 0.78vs. 0.71; 0.83vs. 0.62; 0.83vs. 0.68, all adjusted p-values<0.05) across the three cohorts. The RM offered similar performance to that of TRM (AUCs: 0.61vs. 0.63, adjusted p-value =0.60) while significantly reducing the segmentation time (3.3±3.8 vs. 14.1±4.2 s, p-value <0.001). Under the assistance of FADLM, the accuracies of junior and senior radiologists were improved by 18% and 15% (all adjusted p-values<0.05) and the sensitivities by 25% and 21% (all adjusted p-values<0.05) in the external test cohort. The FADLM with elaborately designed automated strategy using US video keyframes holds good potential to provide an efficient and consistent prediction of cervical LNM in PTC. The FADLM displays superior performance to RM, CSM, and radiologists with promising efficacy.

Impact of CA9 expression in the diagnosis of lymph-node metastases in non-small cell lung cancer based on [18F]FDG PET/CT.

Lung cancer is the leading cause of the global cancer incidence and mortality. It is important to obtain an accurate diagnosis of lymph-node metastasis before surgery to select the therapeutic strategy for non-small cell lung cancer (NSCLC) patients. Carbonic anhydrase 9 (CA9) is considered a marker of hypoxia and it has reported that CA9 is associated with tumor invasion and metastasis. In this study, the correlation between the CA9 expression for lymph-node metastases in NSCLC and [18F]FDG PET/CT results was investigated in order to clarify the efficacy of [18F]FDG PET/CT for detecting lymph-node metastases of NSCLC patients. Among the 564 patients who underwent surgical treatment for NSCLC between 2010 and 2016 at our hospital, a total of 338 patients who underwent preoperative [18F]FDG PET/CT were included in this study. CA9 expression was evaluated by immunochemistry. A lymph node with maximum standardized uptake value (SUVmax) ≥2.5 on [18F]FDG PET/CT was preoperatively defined as a metastatic lymph node. CA9 positivity was detected in 122 patients; the other 216 patients were CA9-negative. The CA9-positive NSCLC cases significantly associated with pleural invasion (p = 0.0063), pT-factor (p = 0.0080), pN-factor (p = 0.036) and pStage (p = 0.043). CA9-positive patients presented significantly poorer survival rate for OS than that of the CA9-negative patients (p = 0.0024). In the multivariable analysis, histological SCC and CA9 positivity were independent poor-prognosis factors for OS. For the total patient population, the sensitivity and specificity of [18F]FDG PET/CT for lymph-node metastases were 54% and 89%, respectively. In contrast, the sensitivity and specificity were particularly low in the CA9-positive SCC cases (36% and 69%, respectively). [18F]FDG PET/CT might not be useful for diagnosing lymph-node metastases of CA9-positive SCC cases of NSCLC.

Lymph node metastasis determined miRNAs in esophageal squamous cell carcinoma.

There is no golden noninvasive and effective technique to diagnose lymph node metastasis (LNM) for esophageal squamous cell carcinoma (ESCC) patients. Here, a classifier was proposed consisting of miRNAs to screen ESCC patients with LNM from the ones without LNM. miRNA expression and clinical data files of 93 ESCC samples were downloaded from TCGA as the discovery set and 119 ESCC samples with similar dataset GSE43732 as the validation set. Differentially expressed miRNAs (DE-miRNAs) were analyzed between patients with LNM and without LNM. LASSO regression was performed for selecting the DE-miRNA pair to consist the classifier. To validate the accuracy and reliability of the classifier, the SVM and AdaBoost algorithms were applied. The CCK-8 and wound healing assay were used to evaluate the role of the miRNA in ESCC cells. There were 43 DE miRNAs between the LNM+ group and LNM- group. Among them, miR-224-5p, miR-99a-5p, miR-100-5p, miR-34c-5p, miR-503-5p, and miR-452-5p were identified by LASSO to establish the classifier. SVM and AdaBoost showed that the model could classify the ESCC patients with LNM from the ones without LNM precisely and reliably in 2 data sets. miR-224-5p in the classifier as the top contributor to discriminate the two groups of patients based on AdaBoost, promoted ESCC cell proliferation and migration in vitro. The classifier based on these 6 miRNAs could classify the ESCC patients with LNM from the ones without LNM successfully.

Lymph node metastasis diagnosis of postoperative OSCC patients by analyzing extracellular vesicles in drainage fluid based on microfluidic isolation

Lymph node metastasis (LNM) is a typical marker in oral squamous cell carcinoma (OSCC) indicating poor prognosis. Pathological examination by artificial image acquisition and analysis, as the main diagnostic method for LNM, often takes a week or longer which may cause great anxiety of the patient and also retard timely treatment. However, there are few efficient fast LNM diagnosis methods in clinical applications currently. Our previous study profiled the proteomics of extracellular vesicles (EVs) derived from postoperative drainage fluid (PDF) and showed the potential of detecting specific EVs that expressed aspartate β-hydroxylase (ASPH) for LNM diagnosis in OSCC patients. Considering that the analysis of ASPH+ PDF-EVs is challenging due to their low abundance (counting less than 10% of total EVs in PDF) and the complex EV isolation process of ultra-centrifugation, we developed a facile platform containing two microfluidic chips filled with antibody-modified microbeads to isolate ASPH+ PDF-EVs, with both the capture and retrieval rate reaching around 90%. Clinical sample analysis based on our method revealed that a mean of 6 × 106 /mL ASPH+ PDF-EVs could be isolated from LNM+ OSCC patients compared to 2.5 × 106 /mL in LNM- OSCC ones. When combined with enzyme-linked immunosorbent assay (ELISA) technique that was commonly used in clinical laboratories in hospitals, this microfluidic platform could precisely distinguish postoperative OSCC patients with LNM or not in several hours, which were validated by a double-blind test containing 6 OSCC patients. We believe this strategy has promise for early diagnosis of LNM in postoperative OSCC patients and finally helps guiding timely and reasonable treatment in clinic.

Diffusion-Weighted Magnetic Resonance Imaging for the Diagnosis of Lymph Node Metastasis in Patients with Biliary Tract Cancer.

Objective: The diagnostic efficacy of the apparent diffusion coefficient (ADC) in diffusion-weighted magnetic resonance imaging (DW-MRI) for lymph node metastasis in biliary tract cancer was investigated in the present study. Methods: In total, 112 surgically resected lymph nodes from 35 biliary tract cancer patients were examined in this study. The mean and minimum ADC values of the lymph nodes as well as the long-axis and short-axis diameters of the lymph nodes were assessed by computed tomography (CT). The relationship between these parameters and the presence of histological lymph node metastasis was evaluated. Results: Histological lymph node metastasis was detected in 31 (27.7%) out of 112 lymph nodes. Metastatic lymph nodes had a significantly larger short-axis diameter compared with non-metastatic lymph nodes (p = 0.002), but the long-axis diameter was not significantly different between metastatic and non-metastatic lymph nodes. The mean and minimum ADC values for metastatic lymph nodes were significantly reduced compared with those for non-metastatic lymph nodes (p < 0.001 for both). However, the minimum ADC value showed the highest accuracy for the diagnosis of histological lymph node metastasis, with an area under the curve of 0.877, sensitivity of 87.1%, specificity of 82.7%, and accuracy of 83.9%. Conclusions: The minimum ADC value in DW-MRI is highly effective for the diagnosis of lymph node metastasis in biliary tract cancer. Accurate preoperative diagnosis of lymph node metastasis in biliary tract cancer should enable the establishment of more appropriate treatment strategies.

Advanced diffusion-weighted MRI models for preoperative prediction of lymph node metastasis in resectable gastric cancer.

To investigate the potential of six advanced diffusion-weighted imaging (DWI) models for preoperative prediction of lymph node metastasis (LNM) in resectable gastric cancer (GC). Between Nov 2022 and Nov 2023, standard MRI scans were prospectively performed in consecutive patients with endoscopic pathology-confirmed gastric adenocarcinoma who were referred for direct radical gastrectomy. Six DWI models, including fractional order calculus (FROC), continuous-time random walk (CTRW), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), the mono-exponential model (MEM) and the stretched exponential model (SEM) were computed. Surgical pathologic diagnosis of LNM was the reference standard, and patients were classified into LNM-positive or LNM-negative groups accordingly. The morphological features and quantitative parameters of the DWI models in different LNM categories were analyzed and compared. Multivariable logistic regression was used to screen significant predictors. Receiver-operating characteristic curves and the area under the curve (AUC) were plotted to evaluate the performances, the Delong test was performed to compare the AUCs. In the LNM-positive group, tumor thickness and kurtosis (DKI_K) were significantly higher, while anomalous diffusion coefficient (CTRW_D), diffusivity (DKI_D), diffusion coefficient (FROC_D), pseudodiffusion coefficient (IVIM_D*), perfusion fraction (IVIM_f), and ADC were lower compared to the LNM-negative group. Clinical tumor staging (cT) and CTRW_D were independent predictors. Their combination demonstrated a superior AUC of 0.930, significantly higher than that of individual parameters. Tumor thickness, DKI_K, CTRW_D, DKI_D, FROC_D, IVIM_D*, IVIM_f and ADC were associated with LNM status. The combination of independent predictors of cT and CTRW_D further enhanced the performance.

296. CLINICAL T1B-SM2-3N0M0 ESOPHAGEAL SQUAMOUS CELL CARCINOMA

Abstract Background With advancements in endoscopic techniques, safer and more reliable endoscopic resection of submucosal invasive lesions has become feasible. The evaluation of treatment outcomes for superficial esophageal carcinoma has typically been predicated on the depth of pathological diagnosis; however, there have been limited studies based on preoperative diagnosis. Given that treatment decisions in clinical practice are primarily guided by preoperative diagnosis, there is a heightened emphasis on studies grounded in preoperative assessments. This study aims to analyze the accuracy of preoperative diagnosis of T1b-SM2-3 N0M0, evaluate the accuracy of treatment selection and depth diagnosis, and assess long-term outcomes based on these determinations. Methods Thirty-eight patients diagnosed with T1b-SM2-3 N0M0 between April 2010 and December 2020 were included in the study. Depth diagnosis was determined through endoscopic evaluation utilizing white light, NBI, NBI magnification plus EUS, with lymph node metastasis diagnosis performed via CT imaging. Treatment decisions were made following patient consent, and long-term prognosis was assessed for each treatment modality. Results Initial treatment involved ESD in 14 patients, with an additional 5 patients undergoing additional treatment (ESD + AD) based on histological evaluation (surgery: 1, CRT: 4). Surgery or neoadjuvant chemotherapy (NAC) followed by surgery was performed in 15 patients, with CRT administered in 7. Among the 22 patients who did not receive NAC and whose histologic depth was evaluable, the accuracy rate of depth diagnosis was 64%, primarily attributable to overdiagnosis. In the 14 patients who underwent ESD excluding piecemeal resection, the negative rate of vertical margins was 93%. Five-year overall survival rates were as follows: ESD: 64.8%, ESD+AD: 75%, surgery: 86.2%, and CRT: 45.7%, with no statistically significant differences observed. The majority of deaths (83%) were attributed to other diseases or cancers, with only 2 deaths attributed to the primary disease. Conclusion ESD or surgery emerged as the preferred treatment modalities in the majority of cases, with relatively favorable long-term prognoses. Treatment decisions were influenced by comorbidities and other factors, potentially impacting long-term outcomes.

Role of diffusion-weighted magnetic resonance imaging in detection of lymph node metastasis in rectal cancer

BackgroundRectal cancer is the most prevalent gastrointestinal tumor. Early diagnosis, accurate staging as well as early treatment are the keys for improving the five-year survival rate. The objective of this research is to assess the effectiveness of diffusion-weighted MRI (DWI) in identifying lymph nodes and distinguishing between benign and metastatic nodes throughout the first stage of primary rectal cancer.ResultsThe study showed that mean ADC value was significantly higher in mucinous carcinoma (1.72 ± 0.36 × 10–3 mm2/sec) than that in non-mucinous carcinoma (0.981 ± 0.276 × 10–3 mm2/sec) with a cutoff value of (1.3 × 10–3) mm2/s which was the precise value to produce high sensitivity, specificity and accuracy of 93%, 94%, and 94%, respectively. ADC analysis showed either intermediate or low signal in 49 (70%) and high signal in 21 (30%) L.Ns. Mean ADC value showed a significant reduction in malignant L.Ns (1.01 ± 0.54 × 10–3 mm2/sec) compared to benign L.Ns (1.51 ± 0.51 × 10–3 mm2/sec), AUC of 0.674 (P = 0.008) and a cutoff value of 0.987 × 10–3 mm2/s with sensitivity, specificity and accuracy of 44.4%, 91.2% and 67.5%, respectively. The mean L.N /tumor ratio was 1.65 ± 0.73 in benign L.Ns and 1.06 ± 0.37 in malignant L.Ns.ConclusionsIn rectal cancer, there was a significant difference between benign and malignant L.Ns regarding diffusion result, L.Ns size, shape, and margin. The study demonstrated the effectiveness of DWI in diagnosing lymph node metastasis in colorectal cancer; true diffusion restriction was significantly noted in malignant L.Ns compared to benign L.Ns. Mean ADC value showed a significant reduction in malignant L.Ns compared to benign L.Ns. L.N/tumor ratio showed a significant reduction in malignant L.Ns compared to benign L.Ns.

Oropharyngeal cancer discovered on diagnosing lymph node metastasis in lung cancer surgery: A case report

Oropharyngeal cancer discovered on diagnosing lymph node metastasis in lung cancer surgery: A case report

Can Highly Specific Ultrasound Criteria Obviate the Need for Sentinel Lymph Node Biopsy in Breast Cancer Patients with Axillary Lymph Node Metastasis?

Background: Sentinel lymph node biopsy (SLNB) is primarily performed to detect axillary lymph node involvement and determine the need for surgical axillary lymph node dissection (ALND). However, the procedure is expensive and necessitates the use of radio-labelled colloids that are not readily available to a large number of patients. It also carries the risk of producing occasional false negative results. Objectives: This study aimed to define highly specific ultrasound parameters to ascertain axillary lymph node involvement as an alternative or complementary method to SLNB in selected patients who require ALND. Patients and Methods: In this prospective cross-sectional study, 256 patients with confirmed breast cancer were selected through non-probability purposive sampling. The selected patients were referred to Omid Hospital, a tertiary educational oncology center in Mashhad, Iran, from 2018 to 2022. This study identified highly specific cut-off points for ultrasound parameters to determine lymph node involvement in comparison with the histological diagnosis post-SLNB or ALND. Measurements were taken of the tumor size, the cortical thickness of the axillary lymph node, the short axis diameter of the node, and the count of lymph nodes with a cortical thickness of ≥ 3 mm. Results: Among 256 patients with a mean age of 46.41 ± 10.77 years, 202 (87.9%) had histologically confirmed metastatic adenopathy. The ultrasound cut-off values identified to define metastasis with high certainty were as follows: A tumor size &gt; 50.5 mm (with 97% specificity), a difference in cortical thickness of ≥ 4.5 mm (with 100% specificity), a short axis diameter of the node &gt; 12 mm (with 95% specificity), more than three lymph nodes with a cortical thickness of ≥ 3 mm (with 94.3% specificity), and a cortical thickness of ≥ 6 mm (with 95% specificity). Conclusion: Highly specific ultrasound findings can diagnose lymph node metastasis with a high degree of certainty and can used as an alternative method to SLNB. A difference of ≥ 4.5 mm between the cortical thickness of the suspected lymph node and the contralateral lymph node and a cortical thickness of ≥ 6 mm are ultrasound findings that can detect lymph nodes involvement, with a specificity ranging from 95% to 100%.

Preoperative computed tomography semantic features in predicting lymph node metastasis of part-solid nodules in non-small cell lung cancer: a multicenter retrospective study.

Lymph node metastasis (LNM) is the most common route of metastasis for lung cancer, and it is an independent risk factor for long-term survival and recurrence in patients with non-small cell lung cancer (NSCLC). The purpose of this study was to explore the value of preoperative computed tomography (CT) semantic features in the differential diagnosis of LNM in part-solid nodules (PSNs) of NSCLC. A total of 955 patients with NSCLC confirmed by postoperative pathology were retrospectively enrolled from January 2019 to March 2023. The clinical, pathological data and preoperative CT images of these patients were investigated and statistically analyzed in order to identify the risk factors for LNM. Multivariate logistic regression was used to select independent risk factors and establish different prediction models. Ten-fold cross-validation was used for model training and validation. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated, and the Delong test was used to compare the predictive performance between the models. LNM occurred in 68 of 955 patients. After univariate analysis and adjustment for confounding factors, smoking history, pulmonary disease, solid component proportion, pleural contact type, and mean diameter were identified as the independent risk factors for LNM. The image predictors model established by the four independent factors of CT semantic features, except smoking history, showed a good diagnostic efficacy for LNM. The AUC in the validation group was 0.857, and the sensitivity, specificity, and accuracy of the model were all 77.6%. Preoperative CT semantic features have good diagnostic value for the LNM of NSCLC. The image predictors model based on pulmonary disease, solid component proportion, pleural contact type, and mean diameter demonstrated excellent diagnostic efficacy and can provide non-invasive evaluation in clinical practice.

Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model

ObjectivesThis study was designed to explore and validate the value of different machine learning models based on ultrasound image-omics features in the preoperative diagnosis of lymph node metastasis in pancreatic cancer (PC).MethodsThis research involved 189 individuals diagnosed with PC confirmed by surgical pathology (training cohort: n = 151; test cohort: n = 38), including 50 cases of lymph node metastasis. Image-omics features were extracted from ultrasound images. After dimensionality reduction and screening, eight machine learning algorithms, including logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), extra trees (ET), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP), were used to establish image-omics models to predict lymph node metastasis in PC. The best omics prediction model was selected through ROC curve analysis. Machine learning models were used to analyze clinical features and determine variables to establish a clinical model. A combined model was constructed by combining ultrasound image-omics and clinical features. Decision curve analysis (DCA) and a nomogram were used to evaluate the clinical application value of the model.ResultsA total of 1561 image-omics features were extracted from ultrasound images. 15 valuable image-omics features were determined by regularization, dimension reduction, and algorithm selection. In the image-omics model, the LR model showed higher prediction efficiency and robustness, with an area under the ROC curve (AUC) of 0.773 in the training set and an AUC of 0.850 in the test set. The clinical model constructed by the boundary of lesions in ultrasound images and the clinical feature CA199 (AUC = 0.875). The combined model had the best prediction performance, with an AUC of 0.872 in the training set and 0.918 in the test set. The combined model showed better clinical benefit according to DCA, and the nomogram score provided clinical prediction solutions.ConclusionThe combined model established with clinical features has good diagnostic ability and can be used to predict lymph node metastasis in patients with PC. It is expected to provide an effective noninvasive method for clinical decision-making, thereby improving the diagnosis and treatment of PC.

New Proposal Strategy of Sentinel Lymph Node Navigation Surgery for Endoscopic Curability C-2: A Retrospective Study.

The purpose of this study was to evaluate the outcomes of the sentinel node navigation surgery (SNNS) followed by limited gastrectomy for early gastric cancer (EGC) with Endoscopic Curability C-2 (eCuraC-2). Between 2001 and 2018, 33 patients were included in this study. Following sentinel node (SN) biopsy using indocyanine green combined with an infrared ray laparoscopic system, limited gastrectomy (LG) [wedge resection (WR), or segmental gastrectomy (SG)] was performed without extended lymphadenectomy. SN detection rate was 97% (32/33). The mean number of SNs per case was 7.8. Three patients (9.1%) with lymph node metastasis (LNM) had a positive SN identified by intraoperative pathological examination. When intraoperative pathologic examination showed SN to be LNM negative, 11 patients underwent WR, and seven were subjected to SG. Postoperative pathological examinations showed no false negatives for LNM, and four patients (12%) had residual cancer in their resected stomachs. Overall survival and disease-specific survival five years after SNNS were 87.9% and 100%, respectively. SNNS followed by LG with lymphatic basin resection may be one of the ideal procedures for patients with eCuraC-2 due to the accurate diagnosis of LNM and favorable disease-specific prognosis.

Preoperative prediction of lymph node metastasis in colorectal cancer using 18F-FDG PET/CT peritumoral radiomics analysis.

Radiomics has been used in the diagnosis of tumor lymph node metastasis (LNM). However, to date, most studies have been based on intratumoral radiomics. Few studies have focused on the use of 18F-fluorodeoxyglucose positron emission computed tomography (18F-FDG PET/CT) peritumoral radiomics for the diagnosis of LNM in colorectal cancer (CRC). Determining the value of radiomics features extracted from 18F-FDG PET/CT images of the peritumoral region in predicting LNM in patients with CRC. The clinical data and preoperative 18F-FDG PET/CT images of 244 CRC patients were retrospectively analyzed. Intratumoral and peritumoral radiomics features were screened using the mutual information method, and least absolute shrinkage and selection operator regression. Based on the selected radiomics features, a radiomics score (Rad-score) was calculated, and independent risk factors obtained from univariate and multivariate logistic regression analyses were used to construct clinical and combined (Radiomics + Clinical) models. The performance of these models was evaluated using the DeLong test, while their clinical utility was assessed by decision curve analysis. Finally, a nomogram was constructed to visualize the predictive model. The most optimal set of features retained by the feature filtering process were all peritumoral radiomic features. Carcinoembryonic antigen levels, PET/CT-reported lymph node status and Rad-score were found to be independent risk factors for LNM. All three LNM risk assessment models exhibited good predictive performance, with the combined model showing the best classification results, with areas under the curve of 0.85 and 0.76 in the training and validation groups, respectively. The DeLong test revealed that the performance of the combined model was superior to that of the clinical and radiomics models in both the training and validation groups, although this difference was only statistically significant in the training group. DCA indicated that the combined model displayed better clinical utility. 18F-FDG PET/CT peritumoral radiomics is uniquely suited to predict the presence of LNM in patients with CRC. In particular, the predictive efficacy of LNM for precision therapy and individualized patient management can be improved by using a combination of clinical risk factors.

Radiological Cutoff Values for Diagnosis of Lymph Node Metastasis in Colorectal Cancer With Multilevel Analysis.

A cutoff value for lymph node diameter in colorectal cancer lymph node metastases has not been established. This prospective study aimed to investigate the direct association between swollen lymph nodes identified on preoperative computed tomography (CT) and pathological findings and proposed a cutoff value. We enrolled patients scheduled to undergo curative surgery with lymph node dissection for colorectal adenocarcinoma who underwent preoperative contrast-enhanced CT and had swollen lymph nodes ≥7 mm in diameter. Two gastrointestinal surgeons intraoperatively identified the target lymph nodes to assess the association between lymph node diameter and pathological findings. The diagnostic performance for lymph node metastasis was determined using multi-level logistic modelling. A total of 109 patients were enrolled, and 225 swollen lymph nodes were pathologically evaluated. Using a cutoff value of ≥9 mm for the short diameter, the positive and negative predictive values, sensitivity, and specificity were 100.0% (99.6%-100.0%), 99.9% (99.1%-100.0%), 62.0% (45.6%-76.0%), and 84.9% (67.0%-94.0%), respectively. The cutoff value for improving the positive predictive value for the preoperative lymph node metastasis diagnosis in colorectal cancer patients should be at least 9 mm in diameter.

Ultrasound-guided fine needle aspiration thyroglobulin in the diagnosis of lymph node metastasis of differentiated papillary thyroid carcinoma and its influencing factors.

Ultrasound-guided fine needle aspiration thyroglobulin (FNA-Tg) is recommended for the diagnosis of lymph node metastasis (LNM) in differentiated thyroid cancer (DTC), but its optimal cutoff value remains controversial, and the effect of potential influencing factors on FNA-Tg levels is unclear. In this study, a retrospective analysis was conducted on 281 patients diagnosed with DTC, encompassing 333 lymph nodes. We analyze the optimal cutoff value and diagnostic efficacy of FNA-Tg, while also evaluating the potential influence of various factors on FNA-Tg. For FNA-Tg, the optimal cutoff value was 16.1 ng/mL (area under the curve (AUC)= 0.942). The optimal cutoff value for FNA-Tg/sTg was 1.42 (AUC = 0.933). The AUC for FNA combined with FNA-Tg yielded the highest value compared to other combined diagnostic methods (AUC = 0.955). It has been found that serum thyroglobulin (sTg) is positively correlated with FNA-Tg (Rs = 0.318), while serum thyroglobulin antibodies (sTgAb) is negatively correlated with FNA-Tg (Rs = -0.147). In cases where the TNM stage indicated N1b, the presence of large or high volume lymph node metastasis(HVLNM), lymph node lateralization/suspicion (L/S) ratio ≤ 2, ultrasound findings indicating lymph node liquefaction, calcification, and increased blood flow, patients with coexisting Hashimoto's thyroiditis (HT), a tumor size ≥10 mm, and postoperative pathology confirming invasion of the thyroid capsule, higher levels of FNA-Tg were observed. However, the subgroup classification of DTC and the presence or absence of thyroid tissue did not demonstrate any significant impact on the levels of FNA-Tg. The findings of this study indicate that the utilization of FNA in conjunction with FNA-Tg is a crucial approach for detecting LNM in DTC. TNM stage indicated N1b, the presence of HVLNM, the presence of HT, lymph node L/S ratio, liquefaction, calcification, tumor diameter, sTg and sTgAb are factors that can impact FNA-Tg levels.In the context of clinical application, it is imperative to individualize the use of FNA-Tg.

FAOT-Net: A 1.5-Stage Framework for 3D Pelvic Lymph Node Detection With Online Candidate Tuning.

Accurate and automatic detection of pelvic lymph nodes in computed tomography (CT) scans is critical for diagnosing lymph node metastasis in colorectal cancer, which in turn plays a crucial role in its staging, treatment planning, surgical guidance, and postoperative follow-up of colorectal cancer. However, achieving high detection sensitivity and specificity poses a challenge due to the small and variable sizes of these nodes, as well as the presence of numerous similar signals within the complex pelvic CT image. To tackle these issues, we propose a 3D feature-aware online-tuning network (FAOT-Net) that introduces a novel 1.5-stage structure to seamlessly integrate detection and refinement via our online candidate tuning process and takes advantage of multi-level information through the tailored feature flow. Furthermore, we redesign the anchor fitting and anchor matching strategies to further improve detection performance in a nearly hyperparameter-free manner. Our framework achieves the FROC score of 52.8 and the sensitivity of 91.7% with 16 false positives per scan on the PLNDataset. Code will be available at: github.com/SCUsomebody/FAOT-Net/.