Identification Of Lymph Node Metastases Research Articles

Efficacy and Diagnostic Reliability of Intraoperative Frozen Section in the Surgical Management of Early-Stage Cervical Cancer

Objectives: The aim of this study of this study was to evaluate preoperative radiology and histopathology findings in cervical cancer lymphadenopathy detection, allowing targeted frozen section examination (FSE). Design: A retrospective analysis was conducted of 203 early-stage cervical cancer patients between 2010 and 2019 in a tertiary centre. Participants/Materials, Setting, and Methods: All patients had histologically confirmed cervical cancer and underwent magnetic resonance imaging (MRI) prior to intraoperative FSE. The primary objectives of the study were to determine the diagnostic accuracy of intraoperative FSE in the identification of lymph node metastases (LNM) in early-stage cervical cancer by correlation with final results obtained using standard histopathology techniques and to examine different preoperative, intraoperative, demographic, radiological, and histopathological factors that could identify those at greatest risk of nodal disease and hence predict those most likely to benefit from FSE, enabling more selective and targeted use. Results: Nineteen patients were found to have LNM (9.36%) at FSE. Patients were at increased risk of LNM by 6-fold with positive LVSI, 3-fold with MRI lymphadenopathy, and 3.5-fold with MRI-visible disease. The presence of lymphadenopathy on MRI and positive LVSI in combination increased the risk of LNM by 19-fold. Limitations: We acknowledge that FSE is expensive and time intensive, exposing patients to increased surgery duration and associated risk. We also recognize that it may not be feasible for all patients. Finally, the analysis is limited by retrospective nature of the study. Conclusions: By application of the preoperative risk stratification algorithm, we may suggest that FSE can be a useful tool in high-risk patients.

Predictors of Postoperative Hypocalcemia and Hypoparathyroidism Following Thyroidectomy in Hanoi

Background: Hypocalcemia is the most frequent complication of thyroid surgeries. In this study, we aimed to describe hypocalcemia after thyroidectomy and evaluate several associated factors. Objectives: This study aimed to describe hypocalcemia after thyroidectomy and evaluate the factors associated with postoperative hypocalcemia. Methods: A cross-sectional study was conducted on 91 patients with thyroid cancer at Hanoi Medical University Hospital. Hypocalcemia was defined as serum calcium levels lower than 2.1 mmol/L, measured 24 hours after surgery. Results: In the postoperative period, 27.5% of the patients exhibited hypocalcemia, with distinct prevalence rates observed between the total thyroidectomy group (47.6%) and the thyroid lobectomy group (10.2%). Concurrently, hypoparathyroidism manifested in 15.4% of the cases. Various factors were identified as contributors to postoperative hypocalcemia, including lymph node metastasis (odds ratio [OR] = 2.6; P < 0.05), total thyroidectomy (OR = 8.0; P < 0.01), diminished parathyroid hormone (PTH) levels (OR = 12.6; P < 0.001), and reduced 25-hydroxyvitamin D3 (25[OH]D3) levels (P < 0.01). Furthermore, multivariate analyses revealed that free thyroxine (FT4) (P = 0.04), 25(OH)D3 (P = 0.037), surgical procedure (P < 0.001), and cancer stage (P < 0.001) independently predicted postoperative hypocalcemia. Notably, our findings underscored a substantial correlation between total thyroidectomy (OR = 21.5, P < 0.001), diminished PTH levels (P < 0.001), and the occurrence of postoperative hypoparathyroidism. Conclusions: The identification of lymph node metastasis, total thyroid surgery, decreased PTH and 25(OH)D3 levels, and albumin concentration are crucial factors in guiding the surgical team to prevent the onset of hypocalcemia.

Fully Automated Identification of Lymph Node Metastases and Lymphovascular Invasion in Endometrial Cancer From Multi-Parametric MRI by Deep Learning.

Early and accurate identification of lymphatic node metastasis (LNM) and lymphatic vascular space invasion (LVSI) for endometrial cancer (EC) patients is important for treatment design, but difficult on multi-parametric MRI (mpMRI) images. To develop a deep learning (DL) model to simultaneously identify of LNM and LVSI of EC from mpMRI images. Retrospective. Six hundred twenty-one patients with histologically proven EC from two institutions, including 111 LNM-positive and 168 LVSI-positive, divided into training, internal, and external test cohorts of 398, 169, and 54 patients, respectively. T2-weighted imaging (T2WI), contrast-enhanced T1WI (CE-T1WI), and diffusion-weighted imaging (DWI) were scanned with turbo spin-echo, gradient-echo, and two-dimensional echo-planar sequences, using either a 1.5 T or 3 T system. EC lesions were manually delineated on T2WI by two radiologists and used to train an nnU-Net model for automatic segmentation. A multi-task DL model was developed to simultaneously identify LNM and LVSI positive status using the segmented EC lesion regions and T2WI, CE-T1WI, and DWI images as inputs. The performance of the model for LNM-positive diagnosis was compared with those of three radiologists in the external test cohort. Dice similarity coefficient (DSC) was used to evaluate segmentation results. Receiver Operating Characteristic (ROC) analysis was used to assess the performance of LNM and LVSI status identification. P value <0.05 was considered significant. EC lesion segmentation model achieved mean DSC values of 0.700 ± 0.25 and 0.693 ± 0.21 in the internal and external test cohorts, respectively. For LNM positive/LVSI positive identification, the proposed model achieved AUC values of 0.895/0.848, 0.806/0.795, and 0.804/0.728 in the training, internal, and external test cohorts, respectively, and better than those of three radiologists (AUC = 0.770/0.648/0.674). The proposed model has potential to help clinicians to identify LNM and LVSI status of EC patients and improve treatment planning. 3 TECHNICAL EFFICACY: Stage 2.

Tumor-to-stroma cd8+ t cells ratio combined with cancer-associated fibroblasts: an innovative approach to predicting lymph node metastases of cervical cancer

PurposePrecise identification of lymph node metastases is vital for the management of cervical cancer. However, the existing diagnostic methods for lymph node metastases have certain drawbacks. In this study, we aim to explore the expression of cancer-associated fibroblasts (CAFs) and tumor-to-stroma CD8+ T cells ratio (CD8+ T cells T:S ratio) and its association with lymph node metastases of cervical cancer.MethodsHundred and ten cervical cancer tissues and 39 biopsy tissues from patients were investigated immunocytochemically for the expression of CAFs and CD8+ T cells. The statistical correlation analysis was carried out using the SPSS system.ResultsA strong and statistically significant negative correlation (r= − 0.690; P < 0.001) was observed between CAF density and CD8+ T cells T:S ratio. Not only were CAFs density and CD8+ T cells T:S ratio correlated with lymph node metastases respectively (P < 0.001), but the combination of them also significantly correlated with lymph node metastases (P < 0.001). Then, we constructed the combined diagnosis model (Logit (P) = − 4.446 + 0.300 × CAFs + 0.752 × CD8+ T cells T:S Ratio) of cervical cancer lymph node metastases. ROC curves analysis showed that the ROC curves areas for CAFs, CD8+ T cells T:S ratio, and a combination of both are 0.879, 0.747, and 0.951. Then, the prediction model was verified by biopsy specimens and consistent results were obtained.ConclusionsThe combination of CAF density and CD8+ T cells T:S ratio has a significant predictive value for lymph node metastases in patients with cervical cancer.

Enzymatic Fat Dissolution Improves Detection of Small Lymph Nodes in Colon Cancer Surgery.

Background Accurate lymph node evaluation is essential for staging colon cancer and guiding postoperative treatment decisions. In this study, we compared the efficacy of a simple enzymatic fat dissolution method with the conventional method for lymph node sampling from specimens after colon cancer surgery. Methods We enrolled 58 patients who underwent elective laparoscopic surgery for colon adenocarcinoma between May 2018 and May 2021 at Fukuoka University Hospital in Fukuoka, Japan. The specimens from these patients were treated using fat dissolution and were compared with specimens from 58 patients for which conventional manual palpation was used. Results A significantly greater number of lymph nodes were detected by the fat dissolution method compared with the conventional method (average per patient, 27.5 vs. 22.6, P = 0.02). In particular, the between-group difference was significant for lymph nodes measuring <5 mm (average per patient, 26.1 vs. 20.9; P = 0.01). Multivariate analysis showed that, compared with the conventional method, the fat dissolution method was significantly associated with the identification of lymph node metastasis. The positive rate of lymph nodes ≥10 mm in diameter was markedly higher along the inferior mesenteric artery than the ileocolic artery (100% vs. 52.6%). Conclusions The use of the fat dissolution method led to an increase in the number of small lymph nodes detected. Rates of metastasis according to lymph node size may depend on the lymph node station.

Contrast-enhanced CT radiomics combined with multiple machine learning algorithms for preoperative identification of lymph node metastasis in pancreatic ductal adenocarcinoma.

This research aimed to assess the value of radiomics combined with multiple machine learning algorithms in the diagnosis of pancreatic ductal adenocarcinoma (PDAC) lymph node (LN) metastasis, which is expected to provide clinical treatment strategies. A total of 128 patients with pathologically confirmed PDAC and who underwent surgical resection were randomized into training (n=93) and validation (n=35) groups. This study incorporated a total of 13 distinct machine learning algorithms and explored 85 unique combinations of these algorithms. The area under the curve (AUC) of each model was computed. The model with the highest mean AUC was selected as the best model which was selected to determine the radiomics score (Radscore). The clinical factors were examined by the univariate and multivariate analysis, which allowed for the identification of factors suitable for clinical modeling. The multivariate logistic regression was used to create a combined model using Radscore and clinical variables. The diagnostic performance was assessed by receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Among the 233 models constructed using arterial phase (AP), venous phase (VP), and AP+VP radiomics features, the model built by applying AP+VP radiomics features and a combination of Lasso+Logistic algorithm had the highest mean AUC. A clinical model was eventually constructed using CA199 and tumor size. The combined model consisted of AP+VP-Radscore and two clinical factors that showed the best diagnostic efficiency in the training (AUC = 0.920) and validation (AUC = 0.866) cohorts. Regarding preoperative diagnosis of LN metastasis, the calibration curve and DCA demonstrated that the combined model had a good consistency and greatest net benefit. Combining radiomics and machine learning algorithms demonstrated the potential for identifying the LN metastasis of PDAC. As a non-invasive and efficient preoperative prediction tool, it can be beneficial for decision-making in clinical practice.

The minimal number of examined lymph nodes for accurate nodal staging and favorable prognosis in T1-2 supraglottic laryngeal squamous cell carcinoma

ObjectiveThe study aimed to investigate the minimal number of examined lymph nodes (ELNs) for accurate assessment of lymph node status and favorable prognosis in patients with stage T1-2 supraglottic laryngeal squamous cell carcinoma (LSCC) who received radical resection. MethodsPatients with stage T1-2 supraglottic LSCC from the Surveillance, Epidemiology, and End Results (SEER) database and the Chinese Academy of Medical Sciences, Cancer Hospital/National Cancer Center (NCC) were reviewed. The association of the ELN count with the identification of nodal metastasis and overall survival (OS) was analyzed using a multivariate regression model. Locally weighted scatterplot smoothing fitting curve and the ‘changepoint’ package were adopted to identify the optimal cut points using R. ResultsA total of 429 patients from the SEER database and 53 patients from NCC were enrolled. The probability of identifying nodal metastasis was positively related to the ELN count. For patients diagnosed with pathological stage N0 (pN0) disease, the mortality risks rapidly decreased when the amount of ELNs exceeded ten, and those with ELNs >10 had better OS. ConclusionAn adequate amount of ELNs benefits precise nodal staging in patients with stage T1-2 supraglottic LSCC. Ten lymph nodes are the minimum number of ELNs. For pN0 patients, an ELN count ≤10 is an unfavorable prognostic factor.

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.

Prototypical multiple instance learning for predicting lymph node metastasis of breast cancer from whole-slide pathological images

Computerized identification of lymph node metastasis of breast cancer (BCLNM) from whole-slide pathological images (WSIs) can largely benefit therapy decision and prognosis analysis. Besides the general challenges of computational pathology, like extra-high resolution, very expensive fine-grained annotation, etc., two particular difficulties with this task lie in (1) modeling the significant inter-tumoral heterogeneity in BCLNM pathological images, and (2) identifying micro-metastases, i.e., metastasized tumors with tiny foci. Towards this end, this paper presents a novel weakly supervised method, termed as Prototypical Multiple Instance Learning (PMIL), to learn to predict BCLNM from WSIs with slide-level class labels only. PMIL introduces the well-established vocabulary-based multiple instance learning (MIL) paradigm into computational pathology, which is characterized by utilizing the so-called prototypes to model pathological data and construct WSI features. PMIL mainly consists of two innovatively designed modules, i.e., the prototype discovery module which acquires prototypes from training data by unsupervised clustering, and the prototype-based slide embedding module which builds WSI features by matching constitutive patches against the prototypes. Relative to existing MIL methods for WSI classification, PMIL has two substantial merits: (1) being more explicit and interpretable in modeling the inter-tumoral heterogeneity in BCLNM pathological images, and (2) being more effective in identifying micro-metastases. Evaluation is conducted on two datasets, i.e., the public Camelyon16 dataset and the Zbraln dataset created by ourselves. PMIL achieves an AUC of 88.2% on Camelyon16 and 98.4% on Zbraln (at 40x magnification factor), which consistently outperforms other compared methods. Comprehensive analysis will also be carried out to further reveal the effectiveness and merits of the proposed method.

Real-Time Monitoring of Colorectal Cancer Location and Lymph Node Metastasis and Photodynamic Therapy Using Fucoidan-Based Therapeutic Nanogel and Near-Infrared Fluorescence Diagnostic-Therapy System.

We report real-time monitoring of colorectal cancer, lymph node metastasis of colorectal cancer cells, and tumor growth inhibition through photodynamic therapy (PDT) using a near-infrared fluorescence diagnostic-therapy system with a light source for PDT and a fucoidan-based theranostic nanogel (CFN-gel) with good accumulation efficiency in cancer cells. To confirm the effect of the fabricated system and developed CFN-gel, in vitro and in vivo experiments were performed. Chlorin e6 (Ce6) and 5-aminolevulinic acid (5-ALA) were used for comparison. We confirmed that CFN-gel has a high accumulation efficiency in cancer cells and high fluorescence signals in near-infrared light for a long period, and only CFN-gel delayed the growth rate of cancer in terms of its size in PDT. In addition, using the near-infrared fluorescence diagnostic-therapy system and CFN-gel prepared for these experiments, the lymph node metastasis of cancer cells was imaged in real time, and the metastasis was confirmed through H&E staining. The possibility of image-guided surgery and identification of lymph node metastasis in colorectal cancer can be confirmed through CFN-gel and a near-infrared fluorescence diagnostic-therapy system that includes various light sources.

A Deep Neural Network–Based Decision Support Tool for the Detection of Lymph Node Metastases in Colorectal Cancer Specimens

The identification of lymph node metastases in colorectal cancer (CRC) specimens is crucial for the planning of postoperative treatment and can be a time-consuming task for pathologists. In this study, we developed a deep neural network (DNN) algorithm for the detection of metastatic CRC in digitized histologic sections of lymph nodes and evaluated its performance as a diagnostic support tool. First, the DNN algorithm was trained using pixel-level annotations of cancerous areas on 758 whole slide images (360 with cancerous areas). The algorithm’s performance was evaluated on 74 whole slide images (43 with cancerous areas). Second, the algorithm was evaluated as a decision support tool on 288 whole slide images covering 1517 lymph node sections, randomized in 16 batches. Two senior pathologists (C.K. and C.O.) assessed each batch with and without the help of the algorithm in a 2 × 2 crossover design, with a washout period of 1 month in between. The time needed for the evaluation of each node section was recorded. The DNN algorithm achieved a median pixel-level accuracy of 0.952 on slides with cancerous areas and 0.996 on slides with benign samples. N+ disease (metastases, micrometastases, or tumor deposits) was present in 103 of the 1517 sections. The algorithm highlighted cancerous areas in 102 of these sections, with a sensitivity of 0.990. Assisted by the algorithm, the median time needed for evaluation was significantly shortened for both pathologists (median time for pathologist 1, 26 vs 14 seconds; P < .001; 95% CI, 11.0-12.0; median time for pathologist 2, 25 vs 23 seconds; P < .001; 95% CI, 2.0-4.0). Our DNN showed high accuracy for detecting metastatic CRC in digitized histologic sections of lymph nodes. This decision support tool has the potential to improve the diagnostic workflow by shortening the time needed for the evaluation of lymph nodes in CRC specimens without impairing diagnostic accuracy.

The impact of lymphadenectomy on intrahepatic cholangiocarcinoma management and prognosis: a comprehensive review

Intrahepatic cholangiocarcinoma (iCCA) is a rare liver cancer generally associated with poor patient outcomes. Curative intent liver resection has been established as a standard treatment of care of resectable disease. However, the role of lymphadenectomy, including the extent of resection and therapeutic value, continues to be an area of controversy. The objective of this review was to highlight the role of lymph node dissection (LND) relative to therapeutic value and prognosis in the surgical management of iCCA. A comprehensive review was performed using MEDLINE/PubMed. Search terms included “intrahepatic cholangiocarcinoma”, “bile duct cancer”, lymphadenectomy”, “lymph node metastasis”, and lymph node staging”. Treatment for iCCA should include an R0 resection with regional lymphadenectomy. The prognostic and therapeutic value of regional lymphadenectomy has been an increased area of research and debate. An increased number of lymph node metastases has correlated with inferior overall survival versus lymph node-negative disease. In addition to surgical resection, regional lymphadenectomy with the removal of at least six lymph nodes in the appropriate nodal basins based on primary tumor location should be standard. The identification of lymph node metastasis provides additional important information to guide providers in determining adjuvant therapy and surveillance strategies.

Deep learning assisted contrast-enhanced CT–based diagnosis of cervical lymph node metastasis of oral cancer: a retrospective study of 1466 cases

ObjectivesLymph node (LN) metastasis is a common cause of recurrence in oral cancer; however, the accuracy of distinguishing positive and negative LNs is not ideal. Here, we aimed to develop a deep learning model that can identify, locate, and distinguish LNs in contrast-enhanced CT (CECT) images with a higher accuracy.MethodsThe preoperative CECT images and corresponding postoperative pathological diagnoses of 1466 patients with oral cancer from our hospital were retrospectively collected. In stage I, full-layer images (five common anatomical structures) were labeled; in stage II, negative and positive LNs were separately labeled. The stage I model was innovatively employed for stage II training to improve accuracy with the idea of transfer learning (TL). The Mask R-CNN instance segmentation framework was selected for model construction and training. The accuracy of the model was compared with that of human observers.ResultsA total of 5412 images and 5601 images were labeled in stage I and II, respectively. The stage I model achieved an excellent segmentation effect in the test set (AP50-0.7249). The positive LN accuracy of the stage II TL model was similar to that of the radiologist and much higher than that of the surgeons and students (0.7042 vs. 0.7647 (p = 0.243), 0.4216 (p < 0.001), and 0.3629 (p < 0.001)). The clinical accuracy of the model was highest (0.8509 vs. 0.8000, 0.5500, 0.4500, and 0.6658 of the Radiology Department).ConclusionsThe model was constructed using a deep neural network and had high accuracy in LN localization and metastasis discrimination, which could contribute to accurate diagnosis and customized treatment planning.Key Points• Lymph node metastasis is not well recognized with modern medical imaging tools.• Transfer learning can improve the accuracy of deep learning model prediction.• Deep learning can aid the accurate identification of lymph node metastasis.

Multidisciplinary Postoperative Validation of 18F-FDG PET/CT Scan in Nodal Staging of Resected Non-Small Cell Lung Cancer.

The aim of this study was to examine the validity of PET/CT scans in the preoperative identification of lymph node metastases (LNM) and compare them with postoperative outcomes. In this retrospective study, we included 87 patients with a solitary lung nodule or biopsy-proven non-small cell lung cancer treated in our institution from 2009 to 2015. Patients were divided into two groups and four subgroups, depending on pre- and postoperative findings. According to our analysis, PET/CT scan has a sensitivity of 50%, a specificity of 88.89%, a positive predictive value of 63.16%, and a negative predictive value of 82.35%. Among the patients, 13.8% were downstaged in PET-CT, while 8% were upstaged. In 78.2% of cases, the PET/CT evaluation was consistent with the histology. Metastases without extracapsular invasion were seldom recognized on PET/CT. This analysis showed the significance of extracapsular tumor invasion, which causes an inflammatory reaction, on LNM, which is probably responsible for preoperative false-positive findings. In conclusion, PET/CT scans are very effective in identifying patients without tumors. Furthermore, it is highly probable that patients with negative findings are free of disease.

A DNA Methylation-based Epigenetic Signature for the Identification of Lymph Node Metastasis in T1 Colorectal Cancer.

This study aimed to unravel the lymph node metastasis (LNM)-related methylated DNA (mDNA) landscape and develop a mDNA signature to identify LNM in patients with T1 colorectal cancers (T1 CRC). Considering the invasiveness of T1 CRC, current guidelines recommend endoscopic resection in patients with LNM-negative, and radical surgical resection only for high-risk LNM-positive patients. Unfortunately, the clinicopathological criteria for LNM risk stratification are imperfect, resulting in frequent misdiagnosis leading to unnecessary radical surgeries and postsurgical complications. We conducted genome-wide methylation profiling of 39 T1 CRC specimens to identify differentially methylated CpGs between LNM-positive and LNM-negative, and performed quantitative pyrosequencing analysis in 235 specimens from 3 independent patient cohorts, including 195 resected tissues (training cohort: n=128, validation cohort: n=67) and 40 pretreatment biopsies. Using logistic regression analysis, we developed a 9-CpG signature to distinguish LNM-positive versus LNM-negative surgical specimens in the training cohort [area under the curve (AUC)=0.831, 95% confidence interval (CI)=0.755-0.892; P <0.0001], which was subsequently validated in additional surgical specimens (AUC=0.825; 95% CI=0.696-0.955; P =0.003) and pretreatment biopsies (AUC=0.836; 95% CI=0.640-1.000, P =0.0036). This diagnostic power was further improved by combining the signature with conventional clinicopathological features. We established a novel epigenetic signature that can robustly identify LNM in surgical specimens and even pretreatment biopsies from patients with T1 CRC. Our signature has strong translational potential to improve the selection of high-risk patients who require radical surgery while sparing others from its complications and expense.

Fluorescence-guided nodal navigation during colectomy for colorectal cancer

Introduction Indocyanine green (ICG) fluorescence is an emerging technique in the surgical field. Among its various applications, it allows surgeons to have real-time visualization of the lymphatic drainage of an organ. The primary outcome of our study is the feasibility and safety of ICG-guided colorectal surgery. Our secondary outcome is the efficacy of ICG-guided lymphadenectomy and whether or not ICG positivity correlates with the identification of nodal metastasis. Material and methods We conducted a single-center prospective study including 32 patients who underwent surgery for colorectal cancer. For each case, ICG was injected into the tumor area either prior to or during surgery. Tumor specimens and excised lymph nodes were analyzed using hematoxylin and eosin (H&E) staining. Results In all patients, ICG injected in the tumor area allowed clear visualization of the lymphatic drainage. A total of 499 lymph nodes were collected, with a mean of 16 per patient. Fifty ICG positive lymph nodes outside the standard lymphadenectomy zone were examined; however, none of the nodes showed signs of nodal metastasis. Conclusions ICG-guided colorectal surgery is safe, feasible and easily reproducible, with a relatively low cost and no radiation exposure. ICG can help the surgeon to visualize the anatomical structures. We did not find an oncological diagnostic advantage in the use of ICG-guided nodal navigation.

Technological Innovations in Thyroid Cancer Surgery

Thyroid cancer is the fifth most common cancer in the USA, with differentiated subtype accounting for more than 95% of neoplasm. Surgery remains the mainstay of treatment, either with lobectomy or total thyroidectomy. In the last decades, many technological innovations have been introduced in this field. The aim of this review is to illustrate the most recent advances regarding the classical surgical approach, particularly regarding hemostatic devices, parathyroid identification with fluorescence systems, intraoperative identification of lymph node metastases, and intraoperative neuromonitoring.

Radiological evaluation of metastatic lymph nodes in carcinoma cervix with emphasis on their infiltrative pattern

Background & objectives:Imaging has been added to the International Federation of Gynecology and Obstetrics (FIGO) 2018 staging system of cervical carcinoma. This study was performed to assess the impact of imaging in staging and to ascertain the prevalence and pattern of nodal metastasis on contrast-enhanced computed tomography (CECT) in patients with cervical carcinoma who were treated based on FIGO 2009 staging system.Methods:This retrospective study was conducted to evaluate all patients with biopsy-proven cervical carcinoma who underwent CECT of abdomen at a tertiary cancer centre in north India from April 2017 to April 2019 and for whom either baseline or follow up scans were available. In patients with enlarged or necrotic lymph nodes, the location, size and pattern of infiltration of adjacent organs were recorded.Results:A total of 602 patients of cervical carcinoma had undergone CT during the study period, of whom 138 (22.9%) underwent CT at baseline and 464 (77.1%) patients during follow up. The FIGO (2009) stage distribution at the time of presentation was stage IB: 109 (18.1%); stage IIA: 14 (2.3%), stage IIB: 118 (19.6%), stage IIIA: 12 (2%), stage IIIB: 277 (46%), stage IVA: 20 (3.3%) and stage IVB: 52 (8.6%). Ninety of the 138 (65.22%) patients underwent a stage shift according to the FIGO 2018 because of the presence of enlarged lymph nodes at baseline scan. Sixteen (2.7%) patients had infiltrative nodal masses most commonly involving the blood vessels (n=14) followed by ureter (n=8), bones (n=5), muscle and bowel (n=3, each). The majority (14/16) of these patients presented with vague abdominal pain, discomfort and vomiting, while two had bone pain.Interpretation & conclusions:CECT at baseline helps in accurately assessing the stage in cervical carcinoma. It helps in the identification of lymph node metastasis in cervical carcinoma, which is crucial for guiding accurate management.

The role of lymph nodes in cervical cancer: incidence and identification of lymph node metastases-a literature review.

Correct identification of patients with lymph node metastasis from cervical cancer prior to treatment is of great importance, because it allows more tailored therapy. Patients may be spared unnecessary surgery or extended field radiotherapy if the nodal status can be predicted correctly. This review captures the existing knowledge on the identification of lymph node metastases in cervical cancer. The risk of nodal metastases increases per 2009 FIGO stage, with incidences in the pelvic region ranging from 2% (stage IA2) to 14-36% (IB), 38-51% (IIA) and 47% (IIB); and in the para-aortic region ranging from 2 to 5% (stage IB), 10-20% (IIA), 9% (IIB), 13-30% (III) and 50% (IV). In addition, age, tumor size, lymph vascular space invasion, parametrial invasion, depth of stromal invasion, histological type, and histological grade are reported to be independent prognostic factors for the risk of nodal metastases. Furthermore, biomarkers can contribute to predict a patient's nodal status, of which the squamous cell carcinoma antigen (SCC-Ag) is currently the most widely used in squamous cell cervical cancer. Still, pre-treatment lymph node assessment is primarily performed by imaging, of which diffusion-weighted magnetic resonance imaging has the highest sensitivity and 2-deoxy-2-[18F]fluoro-D-glucose positron emission computed tomography the highest specificity. Imaging results can be combined with clinical parameters in nomograms to increase the accuracy of predicting positives nodes. Despite all the progress regarding pre-treatment prediction of lymph node metastases in cervical cancer in recent years, prediction rates are not robust enough to safely abandon surgical staging of the pelvic or para-aortic region yet.

Intrapulmonary lymph node (stations 13 and 14) metastasis in peripheral non-small cell lung cancer.

It remains unknown whether dissecting the intrapulmonary lymph nodes (stations 13 and 14) when resecting peripheral non-small cell lung cancer (NSCLC) is necessary for accurate tumor node metastasis (TNM) staging. This study investigated intrapulmonary lymph node dissection (stations 13 and 14) on the pathological staging of peripheral NSCLC and the metastatic pattern of the lymph nodes.This retrospective study included patients with primary peripheral NSCLC who underwent radical dissection between January 2013 and December 2015. The clinical data of patients and examination results of intrapulmonary stations 12, 13, and 14 lymph nodes were analyzed.Of 3019 resected lymph nodes in a total of 234 patients (12.9/patient), 263 (8.7%) had metastasis. Ninety-nine patients had lymph node metastasis (42.3%): 40 (17.1%) were N1, 11 (4.7%) were N2, 48 (20.5%) were both N1 and N2, and 135 (57.7%) had no N1 or N2 metastasis. Sixteen (6.8%) patients had metastasis of stations 13 and/or 14. Metastasis in N1 positive patients of stations 10, 11, 12, 13, and 14 were 2.7%, 10.5%, 9.8%, 10.4%, and 8.5%, respectively. Missed detection without station 13 and 14 dissection was up to 6.8% (16/234).Dissection of stations 13 and 14 could be helpful for the identification of lymph node metastasis and for the accurate TNM staging of primary NSCLC.