scholarly journals Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Xiaodong Wang ◽  
Ying Chen ◽  
Yunshu Gao ◽  
Huiqing Zhang ◽  
Zehui Guan ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Main Method ◽  
Learning Framework ◽  
Prognostic Assessment ◽  
N Stage ◽  
Cancer Outcome ◽  
Whole Slide Images

AbstractN-staging is a determining factor for prognostic assessment and decision-making for stage-based cancer therapeutic strategies. Visual inspection of whole-slides of intact lymph nodes is currently the main method used by pathologists to calculate the number of metastatic lymph nodes (MLNs). Moreover, even at the same N stage, the outcome of patients varies dramatically. Here, we propose a deep-learning framework for analyzing lymph node whole-slide images (WSIs) to identify lymph nodes and tumor regions, and then to uncover tumor-area-to-MLN-area ratio (T/MLN). After training, our model’s tumor detection performance was comparable to that of experienced pathologists and achieved similar performance on two independent gastric cancer validation cohorts. Further, we demonstrate that T/MLN is an interpretable independent prognostic factor. These findings indicate that deep-learning models could assist not only pathologists in detecting lymph nodes with metastases but also oncologists in exploring new prognostic factors, especially those that are difficult to calculate manually.

scholarly journals Identification of nodal micrometastasis in colorectal cancer using deep learning on annotation-free whole-slide images

2021 ◽  
Author(s):  
Wen-Yu Chuang ◽  
Chi-Chung Chen ◽  
Wei-Hsiang Yu ◽  
Chi-Ju Yeh ◽  
Shang-Hung Chang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Colorectal Cancer ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Tumor Size ◽  
Manual Annotation ◽  
Nodal Micrometastasis ◽  
Deep Learning Model ◽  
Whole Slide Images

AbstractDetection of nodal micrometastasis (tumor size: 0.2–2.0 mm) is challenging for pathologists due to the small size of metastatic foci. Since lymph nodes with micrometastasis are counted as positive nodes, detecting micrometastasis is crucial for accurate pathologic staging of colorectal cancer. Previously, deep learning algorithms developed with manually annotated images performed well in identifying micrometastasis of breast cancer in sentinel lymph nodes. However, the process of manual annotation is labor intensive and time consuming. Multiple instance learning was later used to identify metastatic breast cancer without manual annotation, but its performance appears worse in detecting micrometastasis. Here, we developed a deep learning model using whole-slide images of regional lymph nodes of colorectal cancer with only a slide-level label (either a positive or negative slide). The training, validation, and testing sets included 1963, 219, and 1000 slides, respectively. A supercomputer TAIWANIA 2 was used to train a deep learning model to identify metastasis. At slide level, our algorithm performed well in identifying both macrometastasis (tumor size > 2.0 mm) and micrometastasis with an area under the receiver operating characteristics curve (AUC) of 0.9993 and 0.9956, respectively. Since most of our slides had more than one lymph node, we then tested the performance of our algorithm on 538 single-lymph node images randomly cropped from the testing set. At single-lymph node level, our algorithm maintained good performance in identifying macrometastasis and micrometastasis with an AUC of 0.9944 and 0.9476, respectively. Visualization using class activation mapping confirmed that our model identified nodal metastasis based on areas of tumor cells. Our results demonstrate for the first time that micrometastasis could be detected by deep learning on whole-slide images without manual annotation.

BIOPSY OF THE SIGNALING LYMPH NODES IN GASTRIC CANCER - CURRENT STATUS OF THE PROBLEM AND PROSPECTS

2018 ◽  
Vol 64 (3) ◽  
pp. 335-344
Author(s):  
Aleksey Karachun ◽  
Yuriy Pelipas ◽  
Oleg Tkachenko ◽  
D. Asadchaya
Keyword(s):  
Gastric Cancer ◽  
Lymph Node ◽  
Sentinel Lymph Node ◽  
Lymph Nodes ◽  
Stomach Cancer ◽  
Current Status ◽  
Regional Lymph Nodes ◽  
Systematic Analysis ◽  
Tumor Spread ◽  
Special Value

The concept of biopsy of sentinel lymph node as the first lymph node in the pathway of lymphogenous tumor spread has been actively discussed over the past decades and has already taken its rightful place in breast and melanoma surgery. The goal of this method is to exclude vain lymphadenectomy in patients without solid tumor metastases in regional lymph nodes. In the era of minimally invasive and organ-saving operations interventions it seems obvious an idea to introduce a biopsy of sentinel lymph node in surgery of early gastric cancer. Meanwhile the complexity of lymphatic system of the stomach and the presence of so-called skip metastases are factors limiting the introduction of a biopsy of sentinel lymph node in stomach cancer. This article presents a systematic analysis of biopsy technology of signaling lymph node as well as its safety and oncological adequacy. Based on literature data it seems to us that the special value of biopsy of sentinel lymph nodes in the future will be in the selection of personalized surgical tactics for stomach cancer.

scholarly journals Positive lymph node ratio is an index in predicting prognosis for remnant gastric cancer with insufficient retrieved lymph node in R0 resection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Honghu Wang ◽  
Hao Qi ◽  
Xiaofang Liu ◽  
Ziming Gao ◽  
Iko Hidasa ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Lymph Node Ratio ◽  
Prognostic Index ◽  
Staging System ◽  
R0 Resection ◽  
Remnant Gastric Cancer ◽  
Curative Intent ◽  
Node Ratio

AbstractThe staging system of remnant gastric cancer (RGC) has not yet been established, with the current staging being based on the guidelines for primary gastric cancer. Often, surgeries for RGC fail to achieve the > 15 lymph nodes needed for TNM staging. Compared with the pN staging system, lymph node ratio (NR) may be more accurate for RGC staging and prognosis prediction. We retrospectively analyzed the data of 208 patients who underwent R0 gastrectomy with curative intent and who have ≤ 15 retrieved lymph nodes (RLNs) for RGC between 2000 and 2014. The patients were divided into four groups on the basis of the NR cutoffs: rN0: 0; rN1: > 0 and ≤ 1/6; rN2: > 1/6 and ≤ 1/2; and rN3: > 1/2. The 5-year overall survival (OS) rates for rN0, rN1, rN2, and rN3 were 84.3%, 64.7%, 31.5%, and 12.7%, respectively. Multivariable analyses revealed that tumor size (p = 0.005), lymphovascular invasion (p = 0.023), and NR (p < 0.001), but not pN stage (p = 0.682), were independent factors for OS. When the RLN count is ≤ 15, the NR is superior to pN as an important and independent prognostic index of RGC, thus predicting the prognosis of RGC patients more accurately.

scholarly journals The Prognostic Value of Lymph Nodes Dissection Number on Survival of Patients with Lymph Node-Negative Gastric Cancer

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Wu Song ◽  
Yujie Yuan ◽  
Liang Wang ◽  
Weiling He ◽  
Xinhua Zhang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Tumor Invasion ◽  
Prognostic Value ◽  
Operation Time ◽  
Invasion Depth ◽  
Node Negative ◽  
Tumor Invasion Depth

Objective.The study was designed to explore the prognostic value of examined lymph node (LN) number on survival of gastric cancer patients without LN metastasis.Methods.Between August 1995 and January 2011, 300 patients who underwent gastrectomy with D2 lymphadenectomy for LN-negative gastric cancer were reviewed. Patients were assigned to various groups according to LN dissection number or tumor invasion depth. Some clinical outcomes, such as overall survival, operation time, length of stay, and postoperative complications, were compared among all groups.Results.The overall survival time of LN-negative GC patients was50.2±30.5months. Multivariate analysis indicated that LN dissection number(P<0.001)and tumor invasion depth(P<0.001)were independent prognostic factors of survival. The number of examined LNs was positively correlated with survival time(P<0.05)in patients with same tumor invasion depth but not correlated with T1 stage or examined LNs>30. Besides, it was not correlated with operation time, transfusion volume, length of postoperative stay, or postoperative complication incidence(P>0.05).Conclusions.The number of examined lymph nodes is an independent prognostic factor of survival for patients with lymph node-negative gastric cancer. Sufficient dissection of lymph nodes is recommended during surgery for such population.

scholarly journals Development and Validation a Nomogram for Predicting the Third Station Lymph Node Metastasis in Early Gastric Cancer

2021 ◽  
Author(s):  
Xiaoxiao Wang ◽  
Cong Li ◽  
Mengjie Fang ◽  
Liwen Zhang ◽  
Lianzhen Zhong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Validation Cohort ◽  
Predictive Accuracy ◽  
Clinical Risk Factors ◽  
Node Metastasis ◽  
Clinical Risk

Abstract Background:This study aimed to evaluate the value of radiomic nomogram in predicting lymph node metastasis in T1-2 gastric cancer according to the No. 3 station lymph nodes.Methods:A total of 159 T1-2 gastric cancer (GC) patients who had undergone surgery with lymphadenectomy between March 2012 and November 2017 were retrospectively collected and divided into a primary cohort (n = 80) and a validation cohort (n = 79). Radiomic features were extracted from both tumor region and No. 3 station lymph nodes (LN) based on computed tomography (CT) images per patient. Then, key features were selected using minimum redundancy maximum relevance algorithm and fed into two radiomic signatures, respectively. Meanwhile, the predictive performance of clinical risk factors was studied. Finally, a nomogram was built by merging radiomic signatures and clinical risk factors and evaluated by the area under the receiver operator characteristic curve (AUC) as well as decision curve.Results: Two radiomic signatures, reflecting phenotypes of the tumor and LN respectively, were significantly associated with LN metastasis. A nomogram incorporating two radiomic signatures and CT-reported LN metastasis status showed good discrimination of LN metastasis in both the primary cohort (AUC: 0.915; 95% confidence interval [CI]: 0.832-0.998) and validation cohort (AUC: 0.908; 95%CI: 0.814-1.000). The decision curve also indicated its potential clinical usefulness.Conclusions:The nomogram received favorable predictive accuracy in predicting No.3 station LN metastasis in T1-2 GC, and could assist the choice of therapy.

scholarly journals 18F-FDG PET/CT Radiomics for Preoperative Prediction of Lymph Node Metastases and Nodal Staging in Gastric Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Qiufang Liu ◽  
Jiaru Li ◽  
Bowen Xin ◽  
Yuyun Sun ◽  
Dagan Feng ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Lymph Node ◽  
Lymph Node Metastases ◽  
Fdg Pet ◽  
Fixed Ratio ◽  
Preoperative Prediction ◽  
Pet Ct ◽  
N Stage ◽  
Node Metastases ◽  
Fdg Pet Ct

ObjectivesThe accurate assessment of lymph node metastases (LNMs) and the preoperative nodal (N) stage are critical for the precise treatment of patients with gastric cancer (GC). The diagnostic performance, however, of current imaging procedures used for this assessment is sub-optimal. Our aim was to investigate the value of preoperative 18F-FDG PET/CT radiomic features to predict LNMs and the N stage.MethodsWe retrospectively collected clinical and 18F-FDG PET/CT imaging data of 185 patients with GC who underwent total or partial radical gastrectomy. Patients were allocated to training and validation sets using the stratified method at a fixed ratio (8:2). There were 2,100 radiomic features extracted from the 18F-FDG PET/CT scans. After selecting radiomic features by the random forest, relevancy-based, and sequential forward selection methods, the BalancedBagging ensemble classifier was established for the preoperative prediction of LNMs, and the OneVsRest classifier for the N stage. The performance of the models was primarily evaluated by the AUC and accuracy, and validated by the independent validation methods. Analysis of the feature importance and the correlation were also conducted. We also compared the predictive performance of our radiomic models to that with the contrast-enhanced CT (CECT) and 18F-FDG PET/CT.ResultsThere were 185 patients—127 men, 58 women, with the median age of 62, and an age range of 22–86 years. One CT feature and one PET feature were selected to predict LNMs and achieved the best performance (AUC: 82.2%, accuracy: 85.2%). This radiomic model also detected some LNMs that were missed in CECT (19.6%) and 18F-FDG PET/CT (35.7%). For predicting the N stage, four CT features and one PET feature were selected (AUC: 73.7%, accuracy: 62.3%). Of note, a proportion of patients in the validation set whose LNMs were incorrectly staged by CECT (57.4%) and 18F-FDG PET/CT (55%) were diagnosed correctly by our radiomic model.ConclusionWe developed and validated two machine learning models based on the preoperative 18F-FDG PET/CT images that have a predictive value for LNMs and the N stage in GC. These predictive models show a promise to offer a potentially useful adjunct to current staging approaches for patients with GC.

scholarly journals From Raw Pixels to Recurrence Image for Deep Learning of Benign and Malignant Mediastinal Lymph Nodes on Computed Tomography

2020 ◽  
Author(s):  
Tuan Pham
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Mediastinal Lymph Node ◽  
Survival Rates ◽  
Primary Lung Cancer ◽  
Lymph Node Status ◽  
Mediastinal Lymph Nodes

<div>Lung cancer causes the most cancer deaths worldwide and has one of the lowest five-year survival rates of all cancer types. It is reported that more than half of patients with lung cancer die within one year of being diagnosed. Because mediastinal lymph node status is the most important factor for the treatment and prognosis of lung cancer, the aim of this study is to improve the predictive value in assessing the computed tomography (CT) of mediastinal lymph-node malignancy in patients with primary lung cancer. This paper introduces a new method for creating pseudo-labeled images of CT regions of mediastinal lymph nodes by using the concept of recurrence analysis in nonlinear dynamics for the transfer learning. Pseudo-labeled images of original CT images are used as input into deep-learning models. Three popular pretrained convolutional neural networks (AlexNet, SqueezeNet, and DenseNet-201) were used for the implementation of the proposed concept for the classification of benign and malignant mediastinal lymph nodes using a public CT database. In comparison with the use of the original CT data, the results show the high performance of the transformed images for the task of classification. The proposed method has the potential for differentiating benign from malignant mediastinal lymph nodes on CT, and may provide a new way for studying lung cancer using radiology imaging. </div><div><br></div>

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