Hye Ryun Kim, Gahee Ahn, Helen Hong, Bong-Seog Kim
{"title":"利用肿瘤中心注意网络(TCA-Net)加强非小细胞肺癌(NSCLC)患者两年无复发生存期预测","authors":"Hye Ryun Kim, Gahee Ahn, Helen Hong, Bong-Seog Kim","doi":"10.1007/s40846-024-00884-6","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Accurate predictions of postoperative recurrence are essential for determining appropriate follow-up treatments after surgery, as patients with non-small-cell lung cancer (NSCLC) at the same clinical stage have different recurrence incidences. However, simple convolutional neural network (CNN)-based methods are limited when presented with tumors of various sizes. This study aims to predict two-year recurrence-free survival precisely in patients with tumors of various sizes on preoperative CT images using what is termed a Tumor-Centric Attention Network (TCA-Net).</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>The proposed network features dual branches, each with an identical architecture but distinct weights to extract diverse features from CT images and tumor masks simultaneously. The tumor-centric attention module integrates two disparate feature maps at each level to amplify the characteristics of the tumor. All feature maps are concatenated with the finest resolution, enabling the extraction and integration of comprehensive multi-scale features for the complex tumor environment.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>TCA-Net showed an accuracy of 75%, balanced accuracy of 75.05%, specificity of 76.16% and an AUC value of 0.78. These results represent more balanced accuracies by 4.76% and 2.58% compared to ResNet-18 with CT images and dual ResNet-18s with CT images and tumor masks, respectively. Specifically, TCA-Net demonstrated a substantial improvement in the small-sized tumor group, achieving a balanced accuracy of 81.32%, sensitivity of 85.71%, and specificity of 76.92%.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>TCA-Net improved the prediction performance of two-year recurrence-free survival on average across tumors of all sizes, with significant improvements, especially for small-sized tumors.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing Two-Year Recurrence-Free Survival Prediction in Non-Small Cell Lung Cancer (NSCLC) Patients Using Tumor-Centric Attention Network (TCA-Net)\",\"authors\":\"Hye Ryun Kim, Gahee Ahn, Helen Hong, Bong-Seog Kim\",\"doi\":\"10.1007/s40846-024-00884-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Purpose</h3><p>Accurate predictions of postoperative recurrence are essential for determining appropriate follow-up treatments after surgery, as patients with non-small-cell lung cancer (NSCLC) at the same clinical stage have different recurrence incidences. However, simple convolutional neural network (CNN)-based methods are limited when presented with tumors of various sizes. This study aims to predict two-year recurrence-free survival precisely in patients with tumors of various sizes on preoperative CT images using what is termed a Tumor-Centric Attention Network (TCA-Net).</p><h3 data-test=\\\"abstract-sub-heading\\\">Methods</h3><p>The proposed network features dual branches, each with an identical architecture but distinct weights to extract diverse features from CT images and tumor masks simultaneously. The tumor-centric attention module integrates two disparate feature maps at each level to amplify the characteristics of the tumor. All feature maps are concatenated with the finest resolution, enabling the extraction and integration of comprehensive multi-scale features for the complex tumor environment.</p><h3 data-test=\\\"abstract-sub-heading\\\">Results</h3><p>TCA-Net showed an accuracy of 75%, balanced accuracy of 75.05%, specificity of 76.16% and an AUC value of 0.78. These results represent more balanced accuracies by 4.76% and 2.58% compared to ResNet-18 with CT images and dual ResNet-18s with CT images and tumor masks, respectively. Specifically, TCA-Net demonstrated a substantial improvement in the small-sized tumor group, achieving a balanced accuracy of 81.32%, sensitivity of 85.71%, and specificity of 76.92%.</p><h3 data-test=\\\"abstract-sub-heading\\\">Conclusion</h3><p>TCA-Net improved the prediction performance of two-year recurrence-free survival on average across tumors of all sizes, with significant improvements, especially for small-sized tumors.</p>\",\"PeriodicalId\":50133,\"journal\":{\"name\":\"Journal of Medical and Biological Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Medical and Biological Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s40846-024-00884-6\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical and Biological Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s40846-024-00884-6","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
Enhancing Two-Year Recurrence-Free Survival Prediction in Non-Small Cell Lung Cancer (NSCLC) Patients Using Tumor-Centric Attention Network (TCA-Net)
Purpose
Accurate predictions of postoperative recurrence are essential for determining appropriate follow-up treatments after surgery, as patients with non-small-cell lung cancer (NSCLC) at the same clinical stage have different recurrence incidences. However, simple convolutional neural network (CNN)-based methods are limited when presented with tumors of various sizes. This study aims to predict two-year recurrence-free survival precisely in patients with tumors of various sizes on preoperative CT images using what is termed a Tumor-Centric Attention Network (TCA-Net).
Methods
The proposed network features dual branches, each with an identical architecture but distinct weights to extract diverse features from CT images and tumor masks simultaneously. The tumor-centric attention module integrates two disparate feature maps at each level to amplify the characteristics of the tumor. All feature maps are concatenated with the finest resolution, enabling the extraction and integration of comprehensive multi-scale features for the complex tumor environment.
Results
TCA-Net showed an accuracy of 75%, balanced accuracy of 75.05%, specificity of 76.16% and an AUC value of 0.78. These results represent more balanced accuracies by 4.76% and 2.58% compared to ResNet-18 with CT images and dual ResNet-18s with CT images and tumor masks, respectively. Specifically, TCA-Net demonstrated a substantial improvement in the small-sized tumor group, achieving a balanced accuracy of 81.32%, sensitivity of 85.71%, and specificity of 76.92%.
Conclusion
TCA-Net improved the prediction performance of two-year recurrence-free survival on average across tumors of all sizes, with significant improvements, especially for small-sized tumors.
期刊介绍:
The purpose of Journal of Medical and Biological Engineering, JMBE, is committed to encouraging and providing the standard of biomedical engineering. The journal is devoted to publishing papers related to clinical engineering, biomedical signals, medical imaging, bio-informatics, tissue engineering, and so on. Other than the above articles, any contributions regarding hot issues and technological developments that help reach the purpose are also included.