Cancer-free Nodes Research Articles

Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large-scale studies.

The suggestion that the systemic immune response in lymph nodes (LNs) conveys prognostic value for triple-negative breast cancer (TNBC) patients has not previously been investigated in large cohorts. We used a deep learning (DL) framework to quantify morphological features in haematoxylin and eosin-stained LNs on digitised whole slide images. From 345 breast cancer patients, 5,228 axillary LNs, cancer-free and involved, were assessed. Generalisable multiscale DL frameworks were developed to capture and quantify germinal centres (GCs) and sinuses. Cox regression proportional hazard models tested the association between smuLymphNet-captured GC and sinus quantifications and distant metastasis-free survival (DMFS). smuLymphNet achieved a Dice coefficient of 0.86 and 0.74 for capturing GCs and sinuses, respectively, and was comparable to an interpathologist Dice coefficient of 0.66 (GC) and 0.60 (sinus). smuLymphNet-captured sinuses were increased in LNs harbouring GCs (p < 0.001). smuLymphNet-captured GCs retained clinical relevance in LN-positive TNBC patients whose cancer-free LNs had on average ≥2 GCs, had longer DMFS (hazard ratio [HR] = 0.28, p = 0.02) and extended GCs' prognostic value to LN-negative TNBC patients (HR = 0.14, p = 0.002). Enlarged smuLymphNet-captured sinuses in involved LNs were associated with superior DMFS in LN-positive TNBC patients in a cohort from Guy's Hospital (multivariate HR = 0.39, p = 0.039) and with distant recurrence-free survival in 95 LN-positive TNBC patients of the Dutch-N4plus trial (HR = 0.44, p = 0.024). Heuristic scoring of subcapsular sinuses in LNs of LN-positive Tianjin TNBC patients (n = 85) cross-validated the association of enlarged sinuses with shorter DMFS (involved LNs: HR = 0.33, p = 0.029 and cancer-free LNs: HR = 0.21 p = 0.01). Morphological LN features reflective of cancer-associated responses are robustly quantifiable by smuLymphNet. Our findings further strengthen the value of assessment of LN properties beyond the detection of metastatic deposits for prognostication of TNBC patients. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Abstract P5-01-01: Multiscale Deep Learning framework to capture systemic immune features in lymph nodes predictive of triple negative breast cancer outcome

Abstract Systemic immune responses in lymph nodes (LN) convey significant prognostic value for breast cancer patients, which can inform disease progression and optimal treatment management. However, have, so far, not been assessed in large patient cohorts. We have previously shown that morphological alterations in axillary LNs, namely the formation of germinal centres (GCs) in cancer-free LNs, add prognostic value to tumour infiltrating lymphocytes (TILs) in triple-negative breast cancer patients (TNBC) for the development of distant metastasis. Extending manual assessment of LNs beyond the detection of cancer requires the integration of robust deep learning pipelines into the digital pathology workflow. Here, we propose a supervised multiscale deep learning framework named smuLymphNet to capture and quantify GCs and sinuses within LNs from digitised Haematoxylin and Eosin-stained (H&amp;E) whole slide images (WSIs) and show good concordance compared with an inter-pathologist Dice coefficient of manual annotations from four pathologists. The smuLymphNet framework consists of (i) a detection algorithm to determine the boundaries of each LN section on the WSI, using an Otsu-based thresholding method and contouring algorithm; (ii) a supervised multiscale deep learning module for the segmentation of GCs and sinuses; and (iii) quantification of the number, size, and shape of the predicted features. We applied smuLymphNet to a total of 1,800 H&amp;E-stained WSI of &amp;gt;4,000 cancer-free and involved LNs from a retrospectively collected breast cancer cohort collected at Guy’s Hospital (London, UK) from 177 patients (122 N+) enriched for the triple-negative phenotype. A subset of 114 WSI and five breast cancer LN WSIs from each Barts Hospital (London, UK) and Tianjin University Hospital (Tianjin, China) were used to train and evaluate the supervised deep learning module. For training Fully Convolutional Networks (FCNs), WSIs manually annotated for both GCs and sinuses formed a ground-truth set and three FCNs were implemented: (i) a standard U-Net architecture; (ii) a U-Net model with an attention gate mechanism; and (iii) a multiscale-U-Net network (MS U-Net) that encodes, in parallel, a feature representation of the image at multiple resolutions. The MS U-Net achieved the best performance with an average dice score of 0.86 for GCs and 0.74 for sinuses. In comparison, the average dice score amongst four pathologists assessing 24 LN WSI for GCs and sinuses was 0.67 and 0.61, respectively, demonstrating the robustness of the smuLymphNet framework. To establish associations between morphometric immune features and patients’ outcomes, we assessed smuLymphNet captured GCs and sinuses from 686 WSIs from 96 TNBC patients with extensive longitudinal outcome data. We found significant morphological differences in involved and cancer-free LNs between N0 and N+ patients, with the latter displaying larger GCs with more irregular shapes, especially in their involved LNs. Moreover, in alignment with our previously published studies, our multiscale smuLymphNet framework recapitulated and extended the prognostic value of the assessment of GC formation in TNBC N0 patients. We further revealed, for the first time, the prognostic significance of the intranodal lymphatic sinuses when measured in their totality in involved LNs, and the association of alterations in subcapsular sinus areas with superior distant metastasis-free survival in cancer-free and involved LNs in TNBC N+ patients. In summary, smuLymphNet presents a robust multiscale deep learning framework to automatically detect, localise and quantify histopathological immune features in WSI of LNs. By applying smuLymphNet to LNs of TNBC patients from clinical trials, and thereby further evaluating its clinical utility, smuLymphNet could be implemented into the diagnostic digital pathology workflow and, as such, aid in informing on a patient’s disease trajectory. Citation Format: Gregory Verghese, Mengyuan Li, Fangfang Liu, Amit Lohan, Nikhil Cherian, Patrycja Gazinska, Aekta Shah, Aasiyah Oozeer, Cheryl Gillett, Elena Alberts, Thomas Hardiman, Roberto Salgado, Samantha Jones, Louise Jones, Selvam Thavaraj, Sarah E. Pinder, Swapni Rane, Amit Sethi, Anita Grigoriadis. Multiscale Deep Learning framework to capture systemic immune features in lymph nodes predictive of triple negative breast cancer outcome [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-01-01.

Multispectral optoacoustic tomography to differentiate between lymph node metastases and coronavirus-19 vaccine-associated lymphadenopathy.

Worldwide mass vaccination for COVID-19 started in late 2020. COVID-19 vaccines cause benign hypermetabolic lymphadenopathies. Clinical stratification between vaccine-associated benign lymphadenopathies and malignant lymphadenopathies through ultrasound, MRI or FDG PET-CT is not feasible. This leads to unnecessary lymph node biopsies, excisions and even radical lymph node dissections. Therefore, to avoid unnecessary surgeries, we assessed whether noninvasive multispectral optoacoustic tomography (MSOT) enables a better differentiation between benign and malignant lymphadenopathies. All patients were vaccinated for COVID-19. We used MSOT to image deoxy- and oxyhaemoglobin levels in lymph nodes of tumour patients to assess metastatic status. MSOT imaging results were compared with standard ultrasound and pathological lymph node analysis. We also evaluated the influences of gender, age and time between vaccination and MSOT measurement of lymph nodes on the measured deoxy- and oxyhaemoglobin levels in patients with reactive lymph node changes. Multispectral optoacoustic tomography was able to identify cancer-free lymph nodes in vivo without a single false negative (33 total lymph nodes), with 100% sensitivity and 50% specificity. A statistically significant higher deoxyhaemoglobin content was detected in patients with tumour manifestations in the lymph node (p= 0.02). There was no statistically significant difference concerning oxyhaemoglobin (p= 0.65). Age, sex and time between vaccination and MSOT measurement had statistically non-significant impact on deoxy- and oxyhaemoglobin levels in patients with reactive lymph nodes. Here, we show that MSOT measurement is an advantageous clinical approach to differentiate between vaccine-associated benign lymphadenopathy and malignant lymph node metastases based on the deoxygenation level in lymph nodes.

Abstract 6233: A deep learning pipeline to capture the prognostic immune responses in lymph nodes of breast cancer patients

Abstract Capturing tumor infiltrating leucocytes (TILS) and systemic immune responses in breast cancer informs disease progression and optimal treatment management. We have previously shown that morphological alterations in axillary lymph nodes (LNs), namely the formation of germinal centers in cancer-free LNs, adds prognostic value to TILs in triple negative breast cancer patients (TNBC) for the development of distant metastasis. Extending manual assessment of LNs beyond the detection of cancer requires the integration of robust deep learning pipelines into the digital pathology workflow. In this retrospective study, we used 1,100 Haematoxylin &amp; Eosin-stained (H&amp;E) Whole Slide Images (WSI) from Guy’s Hospital (London, UK) of metastatic and cancer-free LNs from 151 patients (100 N+) enriched for triple-negative or HER2-positive breast cancer to implement a supervised deep learning pipeline. A subset of 114 WSI, along with 5 breast cancer LN WSIs from each of Barts Hospital (London, UK) and Tianjin University Hospital (Tianjin, China), and 5 head and neck squamous cell carcinomas LN WSI (Guy’s Hospital) were used to develop, train and evaluate the segmentation task. For training Fully Convolutional Networks (FCNs), WSIs manually annotated for both germinal centers and sinuses formed a ground-truth set. Three FCNs were implemented: (i) a standard U-Net architecture; (ii) a U-Net model with an attention gate mechanism; and (iii) a multiscale-U-Net network (MSA-U-Net) that encodes, in parallel, a feature representation of the image at multiple resolutions. The MSA-U-Net achieved the best performance with an average dice score of 0.85 for germinal centers and 0.75 for sinuses. In comparison, the average dice score amongst 4 pathologists assessing 25 LN WSI for germinal centers and sinuses, was 0.67 and 0.61 respectively, demonstrating the robustness of the MSA-U-Net model. To quantify germinal centers and sinuses in LNs across the entire cohort, the trained MSA-U-Net was used in an inference step on all 1,100 WSI. The detected morphological features were initially localized within LNs using image thresholding and contouring techniques, and quantitatively assessed based on their number, area, shape, and Shannon diversity. We found significant morphological differences in metastatic and cancer-free LNs between N0 and N+ patients, with the latter displaying larger germinal centers with more irregular shapes especially in their metastatic LNs. In addition, we found differences in the Sinus area between LNs containing GCs and those without. Here, we propose a robust deep learning pipeline based on a multiscale FCN framework to automatically detect, localize and quantify histopathological immune features in WSI of LNs. By applying our pipeline to LNs of cancer patients, such as breast or head and neck, in prospective studies or clinical trials, we will further evaluate their prognostic and predictive values. Citation Format: Gregory Verghese, Mengyuan Li, Amit Lohan, Nikhil Cherian, Swapnil Rane, Fangfang Liu, Aekta Shah, Pat Gazinska, Selvam Thavaraj, Amit Sethi, Anita Grigoriadis. A deep learning pipeline to capture the prognostic immune responses in lymph nodes of breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6233.

Systemic immune reaction in axillary lymph nodes adds to tumor-infiltrating lymphocytes in triple-negative breast cancer prognostication

The level of stromal tumor-infiltrating lymphocytes (sTILs) in triple-negative (TNBC) and HER2-positive breast cancers convey prognostic information. The importance of systemic immunity to local immunity is unknown in breast cancer. We previously demonstrated that histological alterations in axillary lymph nodes (LNs) carry clinical relevance. Here, we capture local immune responses by scoring TILs at the primary tumor and systemic immune responses by recording the formation of secondary follicles, also known as germinal centers, in 2,857 cancer-free and involved axillary LNs on haematoxylin and eosin (H&E) stained sections from a retrospective cohort of 161 LN-positive triple-negative and HER2-positive breast cancer patients. Our data demonstrate that the number of germinal center formations across all cancer-free LNs, similar to high levels of TILs, is associated with a good prognosis in low TILs TNBC. This highlights the importance of assessing both primary and LN immune responses for prognostication and for future breast cancer research.

Immune Crosstalk Between Lymph Nodes and Breast Carcinomas, With a Focus on B Cells.

Lymph nodes (LNs) are highly organized secondary lymphoid organs, and reflective of immune responses to infection, injuries, or the presence of cancer. Extensive molecular and morphological analyses of immune and stromal features in tumors and LNs of breast cancer patients have revealed novel patterns indicative of disease progression. Within LNs, there are dynamic structures called germinal centers (GCs), that act as the immunological hubs for B cell development and generation of affinity matured memory B and antibody-producing plasma cells. Acting as a bridge between systemic and local immunity, associations are observed between the frequency of GCs within cancer-free LNs, the levels of stromal tumor infiltrating lymphocytes, and cancer progression. Scattered throughout the tumor microenvironment (TME) or aggregated in clusters forming tertiary lymphoid structures (TLS), the occurrence of tumor infiltrating B cells (TIL-Bs) has been linked mostly to superior disease trajectories in solid cancers. Recent TIL-Bs profiling studies have revealed a plethora of different TIL-B populations, their functional roles, and whether they are derived from GC reactions in the LN, and/or locally from GC-like structures within the TME remains to be investigated. However, parallels between the immunogenic nature of LNs as a pre-metastatic niche, TIL-B populations within the TME, and the presence of TLS will help to decipher local and widespread TIL-Bs responses and their influence on cancer progression to the lymphatics. Therapies that enhance TIL-Bs responses in the LN GC and/or in GC-like structures in the TME are thus emerging management strategies for breast and other cancer patients.

Abstract PO-014: Deep learning-based segmentation accurately captures histological features in cancer-free lymph nodes of breast cancer patients

Abstract Background Histological changes in the cancer-free lymph nodes (LNs) can add to the risk prediction of developing distant metastasis for LN-positive breast cancer patients. We have previously shown that the formation of germinal centres and enlarged sinus are premetastatic features and indicative of superior prognosis. As Whole Slide Images (WSI) demonstrated suitability for detection of LN metastasis with high accuracy based on Convolutional Neural Networks (CNN), we extended the CNN assessments to the detection and segmentation of these premetastatic features. Materials For 136 breast cancer patients, retrospectively collected from Guy’s Hospital, 1,422 Whole Slide Images (WSI) of axillary LNs (containing 1 to 3 LNs per WSI) and primary invasive tumours were 40x scanned by the Hamamatsu Nanozoomer. Two pathologists manually annotated germinal centres and sinuses in 111 of these images facilitating the ground-truth masks for a binary segmentation task using deep learning models. Results Each WSI was split into a series of overlapping patches along with corresponding ground-truth masks at 2.5x, 5x and 10x. Fully Convolutional Neural Networks (FCNs) based on the U-Net architecture were trained on patches from 95 annotated WSI to perform a segmentation for both germinal centres and sinus, with the remaining images used for validation (n= 6) and test sets (n= 10). In addition to a vanilla U-Net encoder-decoder architecture, a model with an attention mechanism to filter feature maps along skip connections was also explored. Additionally, a U-Net with dilated convolutions at each convolution block was used in a multi-scale mechanism that combines features from three different resolutions. To increase the robustness of the FCNs, training data was augmented using random image rotation and different colour perturbation transformations. Predictions were made on regions of WSIs containing whole LNs and were compared with the pathologist annotations on holdout testing images. The Dice coefficient was used to compare the predicted segmentations with the ground-truth annotations. Standard U-Net and attention U-Net architectures achieved an average Dice coefficient of 0.75 across the holdout testing images at 2.5x magnification for germinal centres, with individual images achieving a Dice score above 0.8. Multiscale models achieved the best results at 10x magnification with an average Dice score of 0.85 for germinal centres and 0.75 for sinus. The trained models were then applied to &amp;gt; 1,300 unseen and non-annotated WSIs of axillary LNs. Conclusion Our developed FCN models allow an automated, precise, reproducible and time-efficient approach to annotate germinal centres and sinuses in cancer-free and metastatic LNs. Trained models are currently applied to &amp;gt;1,300 unseen and non-annotated WSIs of axillary LNs, and will demonstrate their comparable performance with manual pathological assessment. Citation Format: Gregory Verghese, Anita Grigoriadis, Amit Sethi, Amit Lohan, Nikhil Cherian, Swati Meena, Harry Chinque, Fangfang Liu, Ed Martin, Tristan Clark, Cheryl Gillet, Julie Owen, Swapnil Rane. Deep learning-based segmentation accurately captures histological features in cancer-free lymph nodes of breast cancer patients [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-014.

Assessments of cancer-free lymph nodes for the prediction of disease progression

Abstract Background Histological changes in the cancer-free lymph nodes (LNs) can add to the risk prediction of developing distant metastasis for LN-positive breast cancer patients. As Whole Slide Images (WSI) demonstrated suitability for detection of LN metastasis with high accuracy based on Convolutional Neural Networks (CNN), we extended the assessments to cancer-free LNs to determine whether early signs of disease progression could be identified. Methods Clinico-pathological data, including level of LNs in the axilla, were available for 143 LN-positive breast cancer patients. All resected LNs were digitised (×40 magnification), totalling to 1,054 WSI. CNN models were based on standard ResNet architecture, and trained using 224x224 sized WSI patches, to classify germinal centres (GC), follicles, sinus histiocytosis, and adipose tissue as defined by 2 independent pathologists. To increase the robustness of the CNN, training data was augmented using image rotation and colour perturbation. A weighted cross entropy was used as a training loss function to account for the imbalance in the four types of patches. Confusion matrix and one-vs-all AUC scores were computed on WSIs patches extracted from held-out test patients to assess the discriminative power of the trained CNN models. Multivariate and cross-validated L2-regularised proportional hazard regression analyses were used for outcome prediction. Results An increased number and smaller sized GCs in cancer-free LNs, the prevalence of metastatic LNs in the low axilla, the location of GCs in metastatic LNs, and lymphocytic infiltration at the tumour invasive front were all indicative of developing distant metastasis in LN-positive breast patients. Metastatic LNs were found exclusively in the low axilla in 77% of patients. Amongst the 913 H&E WSI of cancer-free LNs, 21% presented with GC. Patch level AUC scores of detecting each type of tissue region ranged between 0.82 to 0.94 on held-out patients quantifying the effectiveness of CNN to be used as topological feature detector. Conclusions Addition planned WSI analyses of large breast cancer cohorts will expand on these data, capturing histological features in LNs, with the aim of identifying high-risk and low-risk disease, and aiding in pathological prognostication. Legal entity responsible for the study The authors. Funding Breast Cancer Research Trust, Breast Cancer Now, CRUK. Disclosure All authors have declared no conflicts of interest.

Degree of desmoplasia in metastatic lymph node lesions is associated with lesion size and poor prognosis in pancreatic cancer patients.

Pancreatic cancer is characterized by increased hyperplasia of fibrotic tissue, termed desmoplasia, and lymph node metastasis is an independent prognostic factor in this disease. However, there are no reports focused on desmoplasia in pancreatic cancer lymph node metastases. The present study evaluated a range of factors and investigated their association with poor prognosis in pancreatic cancer cases with lymph node metastasis, including the degree of desmoplasia in lesions. To identify the poor prognostic factors associated with lymph node metastasis, the present study retrospectively reviewed the clinical data of 65 patients with lymph node metastases that underwent surgical pancreatic cancer resection between 2007 and 2012 at a single institution. The investigation focused on the degree of fibrosis in metastatic lesions in 216 lymph nodes, and investigated associations with prognosis or clinicopathological findings. The ratios of the fibrotic area in metastatic lymph node lesions were evaluated and classified into three categories, high (≥70%), moderate (10-70%) and low (<10%). Desmoplasia was not observed in cancer-free lymph nodes. The size of metastatic lymph node lesions was additionally measured, and a significant association between metastatic lesion size and the degree of desmoplasia was observed (P<0.001). The degree of desmoplasia was additionally associated with local extranodal invasion. In the analysis of 65 pancreatic cancer patients with metastatic lymph nodes, the presence of multiple metastatic lymph nodes with moderate or high desmoplasia was significantly associated with poor survival (high, P=0.0048; moderate/high, P=0.0075). Of several clinicopathological factors, the presence of multiple metastatic lymph nodes with high or moderate desmoplasia was associated with overall survival in univariate (P=0.0098) and multivariate (P=0.0466) analyses. The degree of desmoplasia in metastatic lymph nodes is associated with lesion size, and the presence of multiple metastatic lymph nodes with desmoplasia is an independent poor prognostic factor, suggesting that the desmoplasia may have an important role in the malignant progression of lymph node metastases.

Breast Carcinoma–associated Fibroblasts Share Similar Biomarker Profiles in Matched Lymph Node Metastasis

This study sought to understand the role of breast carcinoma-associated fibroblasts in the progression of cancer cells into lymph nodes. We compared fibroblasts of primary tumors and matched the involved lymph nodes to select fibroblast activation markers, namely α-smooth muscle actin (α-SMA), S100A4, and vimentin, as well as to determine the frequency of transforming growth factor β1, a pleiotropic cytokine that induces the differentiation of fibroblasts to myofibroblasts, and its downstream effectors: CXCR4 and p-AKT. We disposed samples of 80 primary invasive ductal carcinomas and matched the involved lymph nodes from 43 cases into 3 tissue microarrays, and analyzed stromal and tumor epithelial cells separately by immunohistochemistry. Control uninvolved lymph nodes were analyzed by whole-tissue sections. Cancer-associated fibroblast in lymph nodes with macrometastasis expressed similar profiles of vimentin, α-SMA, and S100A4 as those found in primary tumors. Cancer-associated fibroblast were uniformly estrogen receptor, progesterone receptor, HER-2, Ki-67, and p53 negative, but expressions of transforming growth factor β1 (TGFβ1), CXCR4, and p-AKT staining (62.3%, 52.4%, 65%, respectively) were equivalent between primary and lymph node metastasis (LNM) fibroblasts. A significant coexpression of TGFβ1 with p-AKT and CXCR4 in LNMs suggested the involvement of these proteins with TGFβ1 signaling. These biomarkers, including α-SMA and S100A4, were negative in fibroblasts of cancer-free lymph nodes, with the exception of vimentin. Our finding that expressions of biological markers were similar in fibroblasts of the primary tumors and in matched LNMs, but were absent in cancer-free lymph nodes, supports the assumption that the lymph node stroma mimics the microenvironment observed in primary tumors.

Three-Dimensional High-Frequency Characterization of Cancerous Lymph Nodes

High-frequency ultrasound (HFU) offers a means of investigating biologic tissue at the microscopic level. High-frequency, three-dimensional (3-D) quantitative-ultrasound (QUS) methods were developed to characterize freshly-dissected lymph nodes of cancer patients. Three-dimensional ultrasound data were acquired from lymph nodes using a 25.6-MHz center-frequency transducer. Each node was inked prior to tissue fixation to recover orientation after sectioning for 3-D histologic evaluation. Backscattered echo signals were processed using 3-D cylindrical regions-of-interest to yield four QUS estimates associated with tissue microstructure ( i.e., effective scatterer size, acoustic concentration, intercept and slope). QUS estimates were computed following established methods using two scattering models. In this study, 46 lymph nodes acquired from 27 patients diagnosed with colon cancer were processed. Results revealed that fully-metastatic nodes could be perfectly differentiated from cancer-free nodes using slope or scatterer-size estimates. Specifically, results indicated that metastatic nodes had an average effective scatterer size ( i.e., 37.1 ± 1.7 μm) significantly larger ( p < 0.05) than that in cancer-free nodes ( i.e., 26 ± 3.3 μm). Therefore, the 3-D QUS methods could provide a useful means of identifying small metastatic foci in dissected lymph nodes that might not be detectable using current standard pathology procedures. (E-mail: mamou@rrinyc.org)

Is the clinical staging system a good choice in the staging of vulvar malignancies?

Dear Sir, We have read the article by Sergi Vidal-Sicart and coworkers [1] entitled “Validation and application of the sentinel lymph node concept in malignant vulvar tumours”, published in the March 2007 issue of the European Journal of Nuclear Medicine and Molecular Imaging. We agree with the authors that sentinel lymph node (SLN) identification permits the accurate pathological examination of regional nodes and could reduce the high morbidity of current surgical treatment in vulvar tumour patients. The results are impressive; however, there are some controversies in the explanation for the tumour stage in SLN-positive cases. The clinical stage of vulvar cancers is at least III when lymph node positivity is found. In the publication of Sergi Vidal-Sicart, however, there are ten patients listed with positive SLN, classified as stage Ib or II. During the past 5 years our team has been involved in the development and application of the method of SLN sampling in vulvar neoplasms to determine the level of progression of the disease. Our preliminary results suggest that sentinel node scintigraphy is a reliable method in early vulvar carcinoma and melanoma [2]. An occasional survey was written by us on the comparison of the TNM and clinical (FIGO) staging systems in different gynaecological malignancies. In 1969 FIGO introduced a staging system for vulvar cancer based on the TNM classification, utilising the clinical assessment of tumour size, tumour site, status of the regional lymph nodes and a limited search for distant metastases. The most serious drawback of this staging system was the inaccuracy of clinical assessment in determining groin node status, especially given that microscopic metastases may be present in clinically normal nodes and cancer-free nodes may be enlarged by inflammation [3]. The TNM system is a dual system distinguishing between a clinical (pretreatment) classification (TNM or cTNM) and a pathological (post-surgical histopathological) classification (pTNM). Thus, in TNM, each patient is staged clinically as well as pathologically. We should realise that not only surgical and pathological, but also clinical staging is important. We should continue to rely upon clinical staging when planning treatment and making prognostic assessments [4]. In the case of vulvar carcinoma, the TNM staging system is obviously superior to clinical (FIGO) staging. Of all the gynaecological malignancies, vulvar carcinoma is the one for which the SLN concept is most relevant. We suggest that in publications dealing with SLN investigations, TNM staging should be preferred over clinical staging. Eur J Nucl Med Mol Imaging (2007) 34:1878–1879 DOI 10.1007/s00259-007-0511-5

Micrometastasis in lymph nodes and microinvasion of the muscularis propria in primary lesions of submucosal gastric cancer

Background: It is important to clarify the clinicopathologic characteristics of micrometastasis in lymph nodes and microinvasion in primary lesions for the treatment options with regard to submucosal gastric cancer. Methods: We examined 1945 lymph nodes and 68 primary tumors resected from 79 patients with submucosal gastric cancer. Two consecutive sections were prepared for simultaneous staining with ordinary hematoxylin and eosin and immunostaining with anticytokeratin antibody (CAM 5.2), respectively. Results: The incidence of nodal involvement in 79 patients with submucosal gastric cancer increased from 13% (10/79 patients) by hematoxylin and eosin staining to 34% (27/79 patients) by cytokeratin immunostaining. Micrometastases in the lymph nodes were found in 17 of 69 patients (25%), with cancer-free nodes examined by hematoxylin and eosin. Microinvasion to the muscularis propria was found in 11 of 68 patients (16%) who were histologically diagnosed with submucosal gastric cancer. Survival analysis demonstrated a lesser 5-year survival in the patients with micrometastasis in lymph nodes (82%) and with microinvasion to muscularis propria (73%). A high incidence of nodal involvement was found in submucosal cancers of large size (>2 cm; 43%), a depressed type (48%), lymphatic invasion (73%), and deeper submucosal invasion (submucosal 3, 53%). A higher incidence of microinvasion was found with the diffuse-type carcinoma (33%). Conclusions: Cytokeratin immunostaining is useful for detecting micrometastasis and microinvasion in submucosal gastric cancer. Tumor size, macroscopic type, lymphatic invasion, and the depth of submucosal invasion are strongly associated with lymph node involvement. (Surgery 1999:126:32-9.)

Human Papillomavirus DNA in Genital Cancers, Metastases, and Lymph Nodes

To evaluate the utility of human papillomavirus (HPV) DNA as molecular marker for diagnostic screening of lymph nodes for cancer metastases, we examined 16 involved and 124 histologically cancer-free nodes of 34 patients with cervical, vulval, or vaginal carcinomas associated with HPV 6, 16, 18, 31, 33, 45, or X. All metastases of HPV-positive tumors contained viral DNA of the same type as the primary cancer but in one case the copy number of viral genomes was dramatically reduced. In a varying proportion of histologically normal nodes of 7 of 23 patients with HPV 16-positive cancers HPV 16 DNA was revealed by Southern blot hybridization and/or PCR. Histologic reevaluation showed a metastasis in one case. Three examples of tumor heterogeneity regarding physical state or copy number of viral DNA are presented. Tumor heterogeneity is discussed as possible explanation for apparently inconsistent results such as HPV-negative metastases of HPV-positive tumors.