Sections Of Sentinel Lymph Node Research Articles

DESCRIPTIVE STUDY OF SENTINEL LYMPH NODES /AXILLARY SAMPLING ON FROZEN SECTION IN CASES OF CARCINOMA OF BREAST

Background: The frozen section provides rapid diagnosis to guide intra or perioperative patient management. Intraoperative frozen sections of sentinel lymph nodes can be used to identify metastatic disease, allowing immediate axillary lymph node dissection. To Study Sentinel Aims: Lymph node /Axillary Sampling on Frozen Section in Cases of Carcinoma of the Breast and to assess the accuracy of frozen section diagnosis. Materials and Methods: - An observational descriptive study was carried out in the department of pathology. A total of 33 patients were evaluated during January 2021 to July 2022. SLNs were examined using standard frozen section procedure and sections were stained with rapid H &E. Patients with positive SLN on FS underwent ALND. The remaining frozen tissue was processed for routine histopathology processing. Results: - Out of 33 cases, 9 (27.27%) cases are positive on the frozen section and 24 (72.72 %) cases are negative on the frozen section. However, on the parafn section, 14 (42.42%) cases are positive and 19 (57.57 %) are negative. There was no false positive case (specicity-100%). 14 cases show tumour deposit in sentinel lymph node. 13 cases had macro-metastatic tumour deposit and 01 case shows micro-metastatic tumour deposit. The diagnostic accuracy is 84.84% and sensitivity 64.28%. Intraoperative frozen section diagnosi Conclusions: - s of sentinel lymph nodes in cases of carcinoma of breast can identify metastatic disease, allowing immediate axillary dissection, if necessary, thus avoiding the need for reoperation. Arm and shoulder morbidity caused by axillary lymph node dissection (ALND) can be avoided in cases where it was not indicated on frozen section.

Deep learning detection of melanoma metastases in lymph nodes

BackgroundIn melanoma patients, surgical excision of the first draining lymph node, the sentinel lymph node (SLN), is a routine procedure to evaluate lymphogenic metastases. Metastasis detection by histopathological analysis assesses multiple tissue levels with hematoxylin and eosin and immunohistochemically stained glass slides. Considering the amount of tissue to analyze, the detection of metastasis can be highly time-consuming for pathologists. The application of artificial intelligence in the clinical routine has constantly increased over the past few years. MethodsIn this multi-center study, a deep learning method was established on histological tissue sections of sentinel lymph nodes collected from the clinical routine. The algorithm was trained to highlight potential melanoma metastases for further review by pathologists, without relying on supplementary immunohistochemical stainings (e.g. anti-S100, anti-MelanA). ResultsThe established method was able to detect the existence of metastasis on individual tissue cuts with an area under the curve of 0.9630 and 0.9856 respectively on two test cohorts from different laboratories. The method was able to accurately identify tumour deposits>0.1 mm and, by automatic tumour diameter measurement, classify these into 0.1 mm to −1.0 mm and>1.0 mm groups, thus identifying and classifying metastasis currently relevant for assessing prognosis and stratifying treatment. ConclusionsOur results demonstrate that AI-based SLN melanoma metastasis detection has great potential and could become a routinely applied aid for pathologists. Our current study focused on assessing established parameters; however, larger future AI-based studies could identify novel biomarkers potentially further improving SLN-based prognostic and therapeutic predictions for affected patients.

Level I axillary dissection in patients with breast cancer and tumor-involved sentinel lymph node after NAC is not sufficient for adequate nodal staging.

The purpose of the study was to investigate the oncological sufficiency of level I axillary dissection for adequate histological nodal staging (ypN) in patients with breast cancer and tumor-involved sentinel lymph node (SLN) after neoadjuvant chemotherapy (NAC). A prospective multicentre pilot study took place from 01.01.2018 to 30.11.2020 in three mammary centres in the Czech Republic in patients with breast cancer after NAC (NCT03556397). Patients in the cohort with positive histological frozen section of SLN were indicated to separate axillary dissection of levels I and II. Sixty-one patients with breast cancer after NAC were included in the study according to inclusion and exclusion criteria. Twelve patients with breast cancer and tumour involved SLN after NAC were further included in the analysis. Two (16.7%) patients had positive non-sentinel lymph nodes in level I only, one (8.3%) patient had positive lymph nodes in level II only, and seven (58.3%) patients had positive lymph nodes in both levels. Level I axillary dissection in a patient with tumour involved SLN after NAC would have resulted in understaging in five (41.7%) patients, mostly ypN1 instead of ypN2. According to our pilot result, level I axillary dissection is not sufficient in terms of adequate histological nodal staging in breast cancer patients after NAC, and level II axillary dissection should not be omitted.

Evaluating the faithfulness of saliency maps in explaining deep learning models using realistic perturbations

Deep Learning has reached human-level performance in several medical tasks including classification of histopathological images. Continuous effort has been made at finding effective strategies to interpret these types of models, among them saliency maps, which depict the weights of the pixels on the classification as an heatmap of intensity values, have been by far the most used for image classification. However, there is a lack of tools for the systematic evaluation of saliency maps, and existing works introduce non-natural noise such as random or uniform values. To address this issue, we propose an approach to evaluate the faithfulness of the saliency maps by introducing natural perturbations in the image, based on oppose-class substitution, and studying their impact on evaluation metrics adapted from saliency models. We validate the proposed approach on a breast cancer metastases detection dataset PatchCamelyon with 327,680 patches of histopathological images of sentinel lymph node sections. Results show that GradCAM, Guided-GradCAM and gradient-based saliency map methods are sensitive to natural perturbations and correlate to the presence of tumor evidence in the image. Overall, this approach proves to be a solution for the validation of saliency map methods without introducing confounding variables and shows potential for application on other medical imaging tasks.

Diagnostic Assessment of Deep Learning Algorithms for Frozen Tissue Section Analysis in Women with Breast Cancer.

Assessing the metastasis status of the sentinel lymph nodes (SLNs) for hematoxylin and eosin-stained frozen tissue sections by pathologists is an essential but tedious and time-consuming task that contributes to accurate breast cancer staging. This study aimed to review a challenge competition (HeLP 2019) for the development of automated solutions for classifying the metastasis status of breast cancer patients. A total of 524 digital slides were obtained from frozen SLN sections: 297 (56.7%) from Asan Medical Center (AMC) and 227 (43.4%) from Seoul National University Bundang Hospital (SNUBH), South Korea. The slides were divided into training, development, and validation sets, where the development set comprised slides from both institutions and training and validation set included slides from only AMC and SNUBH, respectively. The algorithms were assessed for area under the receiver operating characteristic curve (AUC) and measurement of the longest metastatic tumor diameter. The final total scores were calculated as the mean of the two metrics, and the three teams with AUC values greater than 0.500 were selected for review and analysis in this study. The top three teams showed AUC values of 0.891, 0.809, and 0.736 and major axis prediction scores of 0.525, 0.459, and 0.387 for the validation set. The major factor that lowered the diagnostic accuracy was micro-metastasis. In this challenge competition, accurate deep learning algorithms were developed that can be helpful for making a diagnosis on intraoperative SLN biopsy. The clinical utility of this approach was evaluated by including an external validation set from SNUBH.

After neoadjuvant therapy, axillary sentinel lymph node frozen sections from breast cancer patients are accurately diagnosed using telepathology

ContextTelepathology is a digital, microscope-independent method of diagnosing pathology from scanned slides. Frozen sections (FS) can be performed and read by a pathologist at any site. At our institution, telepathology is used for diagnosis of frozen sections of sentinel lymph nodes (SLN) in patients who have undergone neoadjuvant chemotherapy and are enrolled in a clinical trial. ObjectiveWe investigated the accuracy of diagnosing SLN frozen sections in the neoadjuvant setting using telepathology. DesignSLN were entirely submitted for frozen section. A pathology assistant prepared the frozen and scanned the slides using VisionTek M6 digital microscope ecosystem (East Dundee, IL). Cases were interpreted by trained, board-certified pathologists. All frozen sections remnants were submitted for formalin-fixed paraffin-embedded permanent sections. Frozen section diagnoses using telepathology were compared to final pathology. Turn-around time from specimen collection to frozen section diagnosis was recorded. Results54 SLN from 22 breast neoadjuvant cases were diagnosed via telepathology from March 2017 to July 2019. 95% of SLNs interpreted as negative on frozen section and on permanents. A definitive diagnosis could not be rendered on six SLNs; diagnosed “atypical” at frozen. Sensitivity and specificity were 80% and 100% respectively with accuracy of 95.8%. The false-negative rate was 5%. There were no false positives. The average turn-around time was over an hour. ConclusionsTelepathology is an accurate method of diagnosing SLN frozen sections in the neoadjuvant setting, but lobular carcinomas and treatment effect pose diagnostic challenges and the time to report results is increased compared to standard microscopy.

Diagnostic accuracy of frozen section and patterns of nodal spread in high grade endometrial cancer: a secondary analysis of the SENTOR prospective cohort study

<h3>Objectives:</h3> Recent studies have shown that sentinel lymph node (SLN) biopsy accurately detects nodal metastases in patients with high-grade endometrial cancer (EC). SLN biopsy has the potential to replace complete lymphadenectomy for surgical staging in patients with high-grade EC, provided that an SLN algorithm is followed (side-specific pelvic and para-aortic lymphadenectomy for non-mapped hemipelves). The aim of this study was to define the accuracy of intraoperative frozen section for the detection of metastases in SLN biopsy and describe the pattern of lymph node (LN) spread in patients with high-grade EC. <h3>Methods:</h3> We performed a secondary analysis of clinicopathologic data from the SENTOR prospective cohort study evaluating SLN node biopsy at 3 designated cancer centres in Toronto, Canada (ClinicalTrials.gov ID: NCT01886066). Patients with clinical stage I high-grade EC (grade 3 endometrioid, serous, clear cell, carcinosarcoma, undifferentiated, or mixed tumors) undergoing laparoscopic or robotic primary hysterectomy were enrolled. The primary outcome was sensitivity of frozen section of the SLN specimen, compared to a standardized ultrastaging protocol. Secondary outcomes included the pattern and characteristics of LN spread. <h3>Results:</h3> There were 126 patients with high-grade EC with median age 66 years (range: 44-86) and median BMI 26.9 kg/m² (range: 17.6-49.3). Frozen section was performed on surgical specimens from 212 hemipelves; SLNs were identified in 202 specimens (95.7%) and fatty tissue alone was identified in 10 specimens (4.7%). Of the 202 hemipelves in which SLNs were identified, 24 were positive for metastatic disease. Frozen section correctly identified only 12, yielding a sensitivity of 50% (12/24, 95% CI 29.6-70.4) and negative predictive value of 94% (178/202, 95% CI 89-96.5). A total of 24 patients had LN metastases: 16 (13%) had isolated pelvic metastases, 7 (6%) had both pelvic and para-aortic metastases and 1 (0.8%) had an isolated para-aortic metastasis. All para-aortic metastases (n=8) occurred in non-sentinel LNs, among patients with serous (n=5), carcinosarcoma (n=2), and dedifferentiated (n=1) histology. One patient with isolated para-aortic metastases (carcinosarcoma, p53 abnormal) did not have a mapped SLN, but was identified by the SLN algorithm. Nodal metastases occurred in 10% of patients (3/30) with grade 3 endometrioid histology (all pelvic), compared to 33% of patients (15/46) with serous histology (10 pelvic alone, 5 pelvic and para-aortic). <h3>Conclusions:</h3> Intraoperative frozen section of SLNs in high-grade EC patients has poor sensitivity. However, frozen section may still be beneficial to confirm the presence of nodal tissue and ensure adherence to an SLN algorithm, including complete pelvic and para-aortic lymphadenectomy for non-mapped hemipelves. Because isolated para-aortic metastases are rare, para-aortic lymphadenectomy may be omitted in patients in which SLNs were successfully mapped to the pelvis.

Sentinel lymph node intraoperative analysis in endometrial cancer.

Surgical staging in endometrial cancer has evolved and sentinel lymph node (SLN) mapping has replaced a full pelvic and paraaortic lymphadenectomy in several cases. An intraoperative evaluation of SLN might identify patients who could benefit the most from a full lymphadenectomy. The aim of this study is to evaluate the clinical relevance of frozen section of SLN. A retrospective analysis in patients with endometrial cancer who underwent SLN mapping with intraoperative evaluation at frozen section between February 2016 and September 2019 was performed. In case of metastatic involvement, a full lymphadenectomy was performed. Fifty-eight patients met the inclusion criteria. Clinical-pathologic characteristics of the patients and surgical data were analyzed. Overall, bilateral and unilateral detection rates were 100% (58/58), 89.7% (52/58), and 10.3% (6/58), respectively. Eight patients had a stage IIIC disease at permanent section. Frozen section detected SLN metastases in four of eight patients. Of these, two were micrometastases and two were macrometastases. At frozen section of the SLNs, no macrometastases were misdiagnosed. Sensitivity, specificity, accuracy, positive predictive value and negative predictive value of frozen section in detecting metastases was 50%, 100%, 93%, 100% and 92.6%, respectively. The intraoperative evaluation of SLN in endometrial cancer accurately identifies patients with macrometastases. This is the cohort that might benefit the most of a full lymphadenectomy for a higher risk of additional lymph node metastases.

Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer.

PurposeAssessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of SLNs by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin–stained frozen tissue sections of SLNs in breast cancer patients.Materials and MethodsA total of 297 digital slides were obtained from frozen SLN sections, which include post–neoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for 6 weeks with two P40 GPUs. The algorithms were assessed in terms of the area under receiver operating characteristic curve (AUC).ResultsThe top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy.ConclusionIn a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative SLN biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting.

Automatic classification of sentinel lymph node (SLN) metastases in breast carcinoma whole slide image (WSI) through densenet deep learning network

The evaluation of lymph nodes’ metastasis is an important component of Tumor, Node, Metastasis (TNM) breast cancer staging system for better clinical management and treatment. Assessing lymph node metastasis through histologic examination is the most accurate method. This paper proposes significantly advanced and faster image classification Convolutional Neural Network (CNN) model called Densenet-161 for lymph node metastasis. This paper uses pre-processing technique called image thresholding to improve the contrast intensities of the SLN images, which improves the performance of DenseNet. The experimental PCam dataset contains 327,680 patches extracted from 400 Haemotoxylin and Eosin (H&E) stained WSIs of breast cancer with sentinel lymph node sections. The proposed system has generated 94% accuracy for lymph node metastasis classification. Context: Automatic, faster and accurate computational technique implementation for breast cancer sentinel lymph metastases classification in cancer diagnostic pathology. Aims: Automatic sentinel lymph node metastases classification on breast carcinoma WSI through deep learning network, DenseNet-161. Settings and Design: Tumor cells are migrating from a primary metastasize to one or a few lymph nodes, before spreading to other lymph nodes. These few lymph nodes are called as ”sentinel” lymph nodes. The status of these sentinel lymph nodes would accurately predict the status of the remaining lymph nodes. Lymph node status assessment is considered to be one of the most important independent prognostic factors in breast cancer. Methods and Materials: Breast cancer metastasis spreads the tumor cells to other parts of the body, which is predominantly through sentinel lymph node. This paper uses an image classification model DenseNet-161 to classify metastaized and normal SLN images. The DenseNets are significantly advantaged over traditional CNNs, by reducing the vanishing-gradient problem, having feature reusage, strengthening

Safety evaluation of abdominal trachelectomy in patients with cervical tumors ≥2 cm: a single-institution, retrospective analysis.

ObjectiveFor oncologic safety, vaginal radical trachelectomy is generally performed only in patients with cervical cancers smaller than 2 cm. However, because inclusion criteria for abdominal trachelectomy are controversial, we evaluated the safety of abdominal trachelectomy for cervical cancers ≥2 cm.MethodsWe began performing abdominal trachelectomies at our institution in 2005, primarily for squamous cell carcinoma ≤3 cm or adenocarcinoma/adenosquamous carcinoma ≤2 cm. If a positive sentinel lymph node or cervical margin was diagnosed intraoperatively by frozen section, the trachelectomy was converted to a hysterectomy. Medical records of these patients were reviewed retrospectively. Patients who had undergone simple abdominal trachelectomy were excluded from this study.ResultsWe attempted trachelectomy in 212 patients. Among the 135 patients with tumors <2 cm, trachelectomy was successful in 120, one of whom developed recurrence and none of whom died of their disease. Among 77 patients with tumors ≥2 cm, trachelectomy was successful in 62, 2 of whom developed recurrence and 1 of whom died of her disease. The overall relapse rate after trachelectomy was 1.6% (0.8% in <2 cm group and 3.2% in ≥2 cm group), and the mortality rate was 0.5% (0% in <2 cm group and 1.6% in ≥2 cm group). Recurrence-free survival (p=0.303) and overall survival (p=0.193) did not differ significantly between the <2 cm and ≥2 cm groups.ConclusionsAbdominal trachelectomy with intraoperative frozen sections of sentinel lymph nodes and cervical margins is oncologically safe, even in patients with tumors ≥2 cm.

Frozen Sections of Sentinel Lymph Nodes From Breast Cancer Patients Who Undergo Neoadjuvant Therapy Are Accurately Diagnosed Using Telepathology

Abstract Objectives Telepathology enables histologic diagnosis to be made from a scanned slide visualized on a computer. Frozen sections (FSs) can be performed at remote locations and read by a pathologist at a central site. At our institution, qualifying breast cancer patients are enrolled in clinical trials that require FS on sentinel lymph nodes (SLNs) after neoadjuvant therapy (NAT), including chemotherapy or endocrine therapy. In this setting, histology is complicated by treatment effect and biopsy site changes. Others have reported good accuracy of FSs on SLNs after NAT. We investigated whether pathologists are accurate in diagnosing SLN FSs for such cases while using telepathology. To our knowledge, this has not been reported previously. Methods SLNs were entirely submitted and serially sectioned (2-mm thickness). At least two levels were cut. All FSs were submitted for formalin-fixed, paraffin-embedded permanent sections. A pathology assistant at the remote location prepared the FSs and scanned slides using the VisionTek M6 digital microscope ecosystem (East Dundee, IL). Cases were interpreted by board-certified pathologists who completed training on the VisionTek system. For this study, diagnoses from FSs and permanents were compared. Results Forty-five SLNs from 19 breast neoadjuvant cases were read by VisionTek from March 2017 to January 2019. Forty-three cases (96%) called negative by FSs were confirmed negative (on permanents). One hundred percent called positive by FSs were positive. Four SLN called atypical on FSs were positive. Three of these were neoadjuvant endocrine cases for invasive lobular carcinoma. Two cases called atypical were negative. One of these, called atypical/suspicious, resulted in axillary dissection. This case was reviewed by three pathologists at the time of surgery. It had abundant treatment effect, mimicking carcinoma. Conclusion While pitfalls exist, overall, the diagnostic accuracy of frozen section analysis by telepathology of SLNs from breast cases after neoadjuvant therapy is high.

APPLICATION OF DEEP LEARNING ALGORITHMS IN DETECTION OF MITOTIC EVENTS IN ORAL SQUAMOUS CELL CARCINOMA USING CELLPHONE IMAGES

Identifying mitoses in tumors and metastatic deposits in lymph nodes can be laborious and time-consuming tasks. Advances in digital pathology and machine learning algorithms have demonstrated promising results by automating these assignments in breast tissue and sentinel lymph node sections. These breakthroughs have made automated histopathological diagnosis a possibility. All prior studies have used high-resolution images from expensive whole slide image (WSI) scanners for training and detection of cellular events. Our aim was to investigate the efficacy of deep learning algorithms for automated detection of mitotic events on low quality images of oral squamous cell carcinoma (OSCC) produced by cellphone cameras. Methodology A FAST region-based convoluted neural network was trained on WSI from breast cancer. The mitotic events were highlighted through provision of pixel locations to the training algorithm, each patch was approximately 301 × 301 in size. The non-mitosis regions were randomly selected on the images. The final training data set comprised of 4407 image patches. Transfer learning was applied to generate results. Similar algorithms were employed on a data set of comparable size acquired through a cellphone camera from 13 different OSCCs at high-power (40x). Results The WSI demonstrated true positive rates of 0.46 and a false positive of 0.76 with an overall F1 precision of 0.57. The results from cellphone camera showed true positive rates of 0.46, and false positive rates of 0.54. The overall F1 score was 0.49. Conclusion Although WSIs outperformed cellphone images in identifying mitoses, enhancing image quality through modified algorithms may improve efficacy. This will facilitate use of low-cost data sets for training future algorithms for automated detection of cellular events, and widen its impact by making it accessible to every pathologist with a cellphone camera.

Is there a role of frozen section of sentinel lymph node in patients with early breast cancer for the management of axilla in India: a prospective study

Background: Evaluation of axilla using sentinel lymph node biopsy (SLNB) is the standard of care in node negative early breast cancer. Intra operative assessment of SLNB with frozen section (FS) often guides the surgeon regarding decision for level of axillary dissection. The aim of this study was to evaluate accuracy of FS of SLNB in these patients with histopathology examination (HPE) as the gold standard.Methods: This study was performed between July 2017 and November 2018. After gross evaluation of SLNB, nodes were cut in half and frozen; the other half was preserved for HPE. For FS, nodes were sectioned to 4 mm width and examined.Results: A total of 61 patients underwent SLNB, 55 patients undergoing intra-operative FS. The mean age was 53 years (range 30-84, ± 15.09 SD), primary tumor was clinically T1 in 23.6%, T2 in 76.4% patients. A median of four sentinel nodes were identified, mean size 13.84 mm. On FS SLNB was positive for metastasis in 14 (25.5%), on HPE in 16 (29.1%) patients. There were 13 true positive, 38 true negative, 3 false negative and 1 false positive result for FS. The sensitivity, specificity, positive and negative predictive value, false negative and false positive rates were 81.25%, 97.44%, 92.86%, 92.73%, 18.75% and 2.56% respectively in this study. The overall accuracy of FS of SLNB in early carcinoma breast was found to be 92.73%.Conclusions: An intra-operative FS of the SLN in node negative early breast cancer is a highly sensitive tool in axilla management.

Abstract P3-03-43: Incorporation of MSKCC nomogram to guide the application of intra-operative sentinel lymph node frozen section evaluation in patients with early breast cancer

Abstract Background and Objectives: Although de-escalation of axillary surgery becomes more popular, axillary lymph node dissection (ALND) is still the standard care for sentinel lymph node(SLN) positive patients not meeting the criteria of ACOSOG Z0011 in many hospitals, and frozen section (FS) of SLN is one of the valuble intra-operative assesments to avoid axilla re-operation although it was controversial due to accuracy and efficiency concerns. This study was to assess the performance of selective use of frozen section evaluation guided by MSKCC lymph node metastasis risk prediction nomogram to optimize the proceudure to be more accurate and cost effective. Methods: Surgical pathology records of consecutive 2582 biopsies in 2552 patients breast cancer patients from 2011 to 2017 were reviewed, intra-operative frozen section diagnosis were compared to post-operative paraffin reports. We calculated the sensitivity, specificity, accuracy and FNR for different MSKCC risk, the axilla re-operation rate with or without FS and the number needed to treat (NNT) to avoid second ALND was also analyzed. Results: The sensitivity, specificity, and FNR of FS were 84.7%, 99.9%, and 15.3% respectively. The axilla re-operation rates were significantly decreased if FS was done(4.7%±0.4% with FS versus 35.8%±5.8% without FS, P&amp;lt;0.001). The estimated axilla re-operation rate without FS was positively correlated with MSKCC risk(r=0.99, P&amp;lt;0.001), while NNT to avoid second ALND by FS were negatively correlated with MSKCC risk(r=-0.98, P&amp;lt;0.001). When patients were divided into four groups according to quartile MSKCC risk, the axilla re-excisional rates were 18.4%, 25.1%, 38.7%, 58.7% without FS, while 4.8%, 3.2%, 5.6%, 3.2% with FS, and NNT correspondingly decreased from 7.3, 4.5, 3.0 to 1.8. An decision-making algorithm for application of FS was proposed. Conclusion: Stratified decision-making algorithm based on MSKCC prediction model improved the efficiency of FS to avoid axilla re-operatioin in patients undergoing sentinel lymph node biopsy. We recommend FS be restricted to patients with MSKCC risk higher than 0.5 who do not meet ACOSOG Z0011 criteria. Citation Format: Yang H, Wang S, Shen D, Cao Y, Tong F, Chen D, Liu P, Peng Y. Incorporation of MSKCC nomogram to guide the application of intra-operative sentinel lymph node frozen section evaluation in patients with early breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-03-43.

1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset.

BackgroundThe presence of lymph node metastases is one of the most important factors in breast cancer prognosis. The most common way to assess regional lymph node status is the sentinel lymph node procedure. The sentinel lymph node is the most likely lymph node to contain metastasized cancer cells and is excised, histopathologically processed, and examined by a pathologist. This tedious examination process is time-consuming and can lead to small metastases being missed. However, recent advances in whole-slide imaging and machine learning have opened an avenue for analysis of digitized lymph node sections with computer algorithms. For example, convolutional neural networks, a type of machine-learning algorithm, can be used to automatically detect cancer metastases in lymph nodes with high accuracy. To train machine-learning models, large, well-curated datasets are needed.ResultsWe released a dataset of 1,399 annotated whole-slide images (WSIs) of lymph nodes, both with and without metastases, in 3 terabytes of data in the context of the CAMELYON16 and CAMELYON17 Grand Challenges. Slides were collected from five medical centers to cover a broad range of image appearance and staining variations. Each WSI has a slide-level label indicating whether it contains no metastases, macro-metastases, micro-metastases, or isolated tumor cells. Furthermore, for 209 WSIs, detailed hand-drawn contours for all metastases are provided. Last, open-source software tools to visualize and interact with the data have been made available.ConclusionsA unique dataset of annotated, whole-slide digital histopathology images has been provided with high potential for re-use.

A unique gene expression signature is significantly differentially expressed in tumor-positive or tumor-negative sentinel lymph nodes in patients with melanoma.

The purpose of this study was to learn whether molecular characterization through gene expression profiling of node-positive and node-negative sentinel lymph nodes (SLNs) in patients with clinical stage I and II melanoma may improve the understanding of mechanisms of metastasis and identify gene signatures for SLNs/SLNs that correlate with diagnosis or clinical outcome. Gene expression profiling was performed on SLN biopsies of 48 (24 SLN and 24 SLN) patients (T3a/b-T4a/b) who underwent staging of SLNs using transcriptome profiling analysis on 5 μm sections of fresh SLNs. U133A 2.0 Affymetrix gene chips were used. Significance analysis of microarrays was used to test the association between gene expression level and SLN status. Genes with fold change more than 1.5 and q value less than 0.05 were considered differentially expressed. Pathway analysis was performed using Ingenuity Pathway Analysis. The Benjamini and Hochberg method was used to adjust for multiple testing in pathway analysis. We identified 89 probe sets that were significantly differentially expressed (1.5-27-fold; q<0.05). Upon performing the pathway analysis, it was found that 25 genes were common among the most significant and biologically relevant canonical pathways. The molecules and pathways that achieved differential expression of highest statistical significance were notably related to melanoma and its microenvironment and to signaling pathways implicated in immunosuppression and development of cancer. A 25-gene signature is significantly differentially expressed between SLN and SLN and is related to melanoma oncogenesis and immunosuppression. The identified expression profile provides a signature of melanoma nodal involvement. These findings warrant further investigation into the mechanisms of metastasis, melanoma metastasis diagnosis, and prediction of outcome.

“Intraoperative Touch Imprint Cytology of Sentinel Nodes”, Can it Serve as an Alternative to Frozen Section? – A Prospective Comparative Study of Touch Imprint Cytology and Frozen Section in the Intra Operative Assessment of Nodal Metastasis in Carcinoma Breast.

Sentinel lymph node biopsy (SLNB) is now known to offer an excellent alternative method to routine axillary lymph node dissection in staging of breast cancer. However, this minimally invasive technique requires facilities for frozen section (FS) and intraoperative analysis, which are sadly not available at most centers in India. At the same time Touch imprint cytology (TIC), is relatively less cumbersome technique for intraoperative assessment which may provide a fast and accurate method for intraoperative assessment of sentinel lymph nodes. Aim: This study was done to assess the accuracy and feasibility of using the TIC as an alternative to FS to assess metastasis in the sentinel node. Methods: Touch imprint cytological analysis of sentinel lymph nodes was compared prospectively with frozen section and the findings obtained on routine paraffin sections. Touch imprint slides from 32 consecutive women undergoing mastectomies (from Jan 2014 to May 2016) for invasive breast cancer (all 32 were T1-T3 and with clinically N0 nodal status) were prepared during operation from multiple sections of sentinel lymph nodes, stained with haematoxylin and eosin. Results: The sensitivity of TIC was 89.47% (95% CI, 68.49-98.81%), when compared with the final histopathological result as a gold standard method. The sensitivity of the FS was 94.74% (95% CI 73.97% to 99.87%), when compared to the HPE results. The specificity of the TIC and FS when compared to HPE is 100%. Conclusion: This study has lent credibility to the possible use of TIC as an assessment tool of the sentinel node in centers where facilities for frozen section are not available and hence expanding the population of potential beneficiaries who can be spared of morbidity associated with axillary dissection

Sentinel lymph nodes for breast carcinoma: an update on current practice.

Sentinel lymph node (SLN) biopsy has been established as the standard of care for axillary staging in patients with invasive breast carcinoma and clinically negative lymph nodes (cN0). Historically, all patients with a positive SLN underwent axillary lymph node dissection (ALND). The ACOSOG Z0011 trial showed that women with T1-T2 disease and cN0 who undergo breast-conserving surgery and whole-breast radiotherapy can safely avoid ALND. The main goal of SLN examination should be to detect all macrometastases (>2 mm). Gross sectioning of SLNs at 2-mm intervals and microscopic examination of one haematoxylin and eosin-stained section from each SLN block is the preferred method for pathological evaluation of SLNs. The role and timing of SLN biopsy for patients who have received neoadjuvant chemotherapy is controversial, and continues to be explored in clinical trials. SLN biopsies from patients with invasive breast carcinoma who have received neoadjuvant chemotherapy pose particular challenges for pathologists.

Intranodal Mapping Using Carbon Dye Results in More Accurate Lymph Node Staging in Colon Cancer Patients.

Small nodal tumor infiltrates (SNTI)-defined as isolated tumor cells and micrometastases-are associated with worse disease-free and overall survival in stage I and II colon cancer patients. Their detection, however, remains challenging. The objective of the present study was to evaluate whether there is a correlation between the location of SNTI and phagocytosed carbon dye particles in sentinel lymph nodes (SLN) of colon cancer patients. Isosulfan blue and carbon dye were injected intraoperatively near the tumor to mark the SLN. Serial sections of SLN were stained with hematoxylin-eosin and immunohistochemistry. Intranodal distribution of phagocytosed carbon particles was compared to the presence of SNTI. Of a cohort of 159 patients, 24 patients had SNTI in their lymph nodes (LN). SNTI were found in a total of 116 LN of which 66 were SLN and 50 were non-SLN. In 59, these 116 LN with SNTI phagocytosed carbon dye were found (50.9 %). Phagocytosed carbon dye was identified significantly more often in SLN (49 of 66 SNTI positive SLN) compared to 10 of 50 SNTI positive non-SLN (p < 0.001). In 52 out of 59 LN (88.1 %), phagocytosed carbon dye was in close proximity to SNTI. In the majority of patients, SNTI are located in the same SLN compartment as phagocytosed carbon dye particles. Our investigation provides evidence that the use of carbon dye facilitates SNTI detection and improves LN staging in colon cancer. Therefore, the concept of intranodal mapping-which has been previously described for melanoma-can be extended to colon cancer patients.