Dataset Information

Deep Convolutional Neural Network-Based Lymph Node Metastasis Prediction for Colon Cancer Using Histopathological Images.

ABSTRACT:

Background

Human evaluation of pathological slides cannot accurately predict lymph node metastasis (LNM), although accurate prediction is essential to determine treatment and follow-up strategies for colon cancer. We aimed to develop accurate histopathological features for LNM in colon cancer.

Methods

We developed a deep convolutional neural network model to distinguish the cancer tissue component of colon cancer using data from the tissue bank of the National Center for Tumor Diseases and the pathology archive at the University Medical Center Mannheim, Germany. This model was applied to whole-slide pathological images of colon cancer patients from The Cancer Genome Atlas (TCGA). The predictive value of the peri-tumoral stroma (PTS) score for LNM was assessed.

Results

A total of 164 patients with stages I, II, and III colon cancer from TCGA were analyzed. The mean PTS score was 0.380 (± SD = 0.285), and significantly higher PTS scores were observed in patients in the LNM-positive group than those in the LNM-negative group (P < 0.001). In the univariate analyses, the PTS scores for the LNM-positive group were significantly higher than those for the LNM-negative group (P?P < 0.001 and P < 0.001).

Conclusion

We established the PTS score, a simplified reproducible parameter, for predicting LNM in colon cancer using computer-based analysis that could be used to guide treatment decisions. These findings warrant further confirmation through large-scale prospective clinical trials.

SUBMITTER: Kwak MS

PROVIDER: S-EPMC7838556 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Deep Convolutional Neural Network-Based Lymph Node Metastasis Prediction for Colon Cancer Using Histopathological Images.

Kwak Min Seob MS Lee Hun Hee HH Yang Jae Min JM Cha Jae Myung JM Jeon Jung Won JW Yoon Jin Young JY Kim Ha Il HI

Frontiers in oncology 20210113

<h4>Background</h4>Human evaluation of pathological slides cannot accurately predict lymph node metastasis (LNM), although accurate prediction is essential to determine treatment and follow-up strategies for colon cancer. We aimed to develop accurate histopathological features for LNM in colon cancer.<h4>Methods</h4>We developed a deep convolutional neural network model to distinguish the cancer tissue component of colon cancer using data from the tissue bank of the National Center for Tumor Dis ...[more]

PMID: 33520727

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Project description:Histopathological images contain rich phenotypic descriptions of the molecular processes underlying disease progression. Convolutional neural networks, state-of-the-art image analysis techniques in computer vision, automatically learn representative features from such images which can be useful for disease diagnosis, prognosis, and subtyping. Hepatocellular carcinoma (HCC) is the sixth most common type of primary liver malignancy. Despite the high mortality rate of HCC, little previous work has made use of CNN models to explore the use of histopathological images for prognosis and clinical survival prediction of HCC. We applied three pre-trained CNN models-VGG 16, Inception V3 and ResNet 50-to extract features from HCC histopathological images. Sample visualization and classification analyses based on these features showed a very clear separation between cancer and normal samples. In a univariate Cox regression analysis, 21.4% and 16% of image features on average were significantly associated with overall survival (OS) and disease-free survival (DFS), respectively. We also observed significant correlations between these features and integrated biological pathways derived from gene expression and copy number variation. Using an elastic net regularized Cox Proportional Hazards model of OS constructed from Inception image features, we obtained a concordance index (C-index) of 0.789 and a significant log-rank test (p = 7.6E-18). We also performed unsupervised classification to identify HCC subgroups from image features. The optimal two subgroups discovered using Inception model image features showed significant differences in both overall (C-index = 0.628 and p = 7.39E-07) and DFS (C-index = 0.558 and p = 0.012). Our work demonstrates the utility of extracting image features using pre-trained models by using them to build accurate prognostic models of HCC as well as highlight significant correlations between these features, clinical survival, and relevant biological pathways. Image features extracted from HCC histopathological images using the pre-trained CNN models VGG 16, Inception V3 and ResNet 50 can accurately distinguish normal and cancer samples. Furthermore, these image features are significantly correlated with survival and relevant biological pathways.

Project description:BACKGROUND:Papillary thyroid carcinoma is a type of indolent tumor with a dramatically increasing incidence rate and stably high survival rate. Reducing the overdiagnosis and overtreatment of papillary thyroid carcinoma is clinically emergent and important. A radiomics model is proposed in this article to predict lymph node metastasis, the most important risk factor of papillary thyroid carcinoma, based on noninvasive routine preoperative ultrasound images. METHODS:Four hundred fifty ultrasound manually segmented images of patients with papillary thyroid carcinoma with lymph node status obtained from pathology report were enrolled in our retrospective study. A radiomics evaluation of 614 high-throughput features were calculated, including size, shape, margin, boundary, orientation, position, echo pattern, posterior acoustic pattern, and calcification features. Then, combined feature selection strategy was used to select features with the greatest ability to discriminate lymph node status. A support vector machine classifier was employed to build and validate the prediction model. Another independent testing cohort was used to further evaluate the performance of the radiomics model. RESULTS:Among 614 radiomics features, 50 selected features most reflecting echo pattern, posterior acoustic pattern, and calcification showed the superior lymph node status distinguishable performance with area under the receiver operating characteristic curve of 0.753, 0.740, and 0.743 separately when using each type of features predicting the lymph node status. The results of model based on all 50 final features predicting the lymph node status shown an area under the receiver operating characteristic curve of 0.782, and accuracy of 0.712. In the independent testing cohort, the proposed approach showed similar results, with area under the receiver operating characteristic curve of 0.727 and accuracy of 0.710. CONCLUSION:Papillary thyroid carcinoma with lymph node metastasis usually shows a complex echo pattern, posterior region homogeneity, and macrocalcification or multiple calcification. The radiomics model proposed in this article is a promising method for assessing the risk of papillary thyroid carcinoma metastasis noninvasively.

Dataset Information

Deep Convolutional Neural Network-Based Lymph Node Metastasis Prediction for Colon Cancer Using Histopathological Images.

Background

Methods

Results

Conclusion

Publications

Deep Convolutional Neural Network-Based Lymph Node Metastasis Prediction for Colon Cancer Using Histopathological Images.

Similar Datasets

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