Enhancing Two-Year Recurrence-Free Survival Prediction in Non-Small Cell Lung Cancer (NSCLC) Patients Using Tumor-Centric Attention Network (TCA-Net)

IF 1.6 4区 医学 Q4 ENGINEERING, BIOMEDICAL
Hye Ryun Kim, Gahee Ahn, Helen Hong, Bong-Seog Kim
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Abstract

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.

Abstract Image

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
目的 由于处于同一临床阶段的非小细胞肺癌(NSCLC)患者的复发率不同,因此准确预测术后复发对于确定术后适当的后续治疗至关重要。然而,当遇到不同大小的肿瘤时,基于卷积神经网络(CNN)的简单方法就会受到限制。本研究旨在利用所谓的 "以肿瘤为中心的注意力网络"(TCA-Net),准确预测术前 CT 图像中不同大小肿瘤患者的两年无复发生存率。以肿瘤为中心的注意力模块在每个级别整合了两个不同的特征图,以放大肿瘤的特征。所有特征图均以最精细的分辨率进行串联,从而能够针对复杂的肿瘤环境提取和整合全面的多尺度特征。结果TCA-Net 的准确率为 75%,均衡准确率为 75.05%,特异性为 76.16%,AUC 值为 0.78。与使用 CT 图像的 ResNet-18 以及使用 CT 图像和肿瘤掩膜的双 ResNet-18 相比,这些结果表明均衡准确率分别提高了 4.76% 和 2.58%。结论TCA-Net 平均提高了各种大小肿瘤的两年无复发生存率预测性能,尤其是对小肿瘤的预测性能有显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.30
自引率
5.00%
发文量
81
审稿时长
3 months
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.
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