Novel Artificial Intelligence Combining Convolutional Neural Network and Support Vector Machine to Predict Colorectal Cancer Prognosis and Mutational Signatures From Hematoxylin and Eosin Images

IF 7.1 1区 医学 Q1 PATHOLOGY Modern Pathology Pub Date : 2024-07-15 DOI:10.1016/j.modpat.2024.100562
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引用次数: 0

Abstract

Reducing recurrence following radical resection of colon cancer without overtreatment or undertreatment remains a challenge. Postoperative adjuvant chemotherapy (Adj) is currently administered based solely on pathologic TNM stage. However, prognosis can vary significantly among patients with the same disease stage. Therefore, novel classification systems in addition to the TNM are necessary to inform decision-making regarding postoperative treatment strategies, especially stage II and III disease, and minimize overtreatment and undertreatment with Adj. We developed a prognostic prediction system for colorectal cancer using a combined convolutional neural network and support vector machine approach to extract features from hematoxylin and eosin staining images. We combined the TNM and our artificial intelligence (AI)–based classification system into a modified TNM-AI classification system with high discriminative power for recurrence-free survival. Furthermore, the cancer cell population recognized by this system as low risk of recurrence exhibited the mutational signature SBS87 as a genetic phenotype. The novel AI-based classification system developed here is expected to play an important role in prognostic prediction and personalized treatment selection in oncology.

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结合 CNN 和 SVM 的新型人工智能从 HE 图像预测结直肠癌预后和突变特征
减少结肠癌根治性切除术后的复发,同时避免治疗过度或治疗不足仍是一项挑战。术后辅助化疗(Adj)目前仅根据病理肿瘤、结节和转移(TNM)分期进行。然而,疾病分期相同的患者预后可能会有很大差异。因此,有必要在 TNM 分期之外建立新的分类系统,以便为术后治疗策略(尤其是 II 期和 III 期疾病)的决策提供依据,并最大限度地减少 Adj 的过度治疗和治疗不足。 我们开发了一种结直肠癌预后预测系统,该系统采用卷积神经网络(CNN)和支持向量机(SVM)相结合的方法,从血沉和伊红染色(HE)图像中提取特征。我们将 TNM 和基于人工智能的分类系统结合成 TNM-AI(mTNM-AI)分类系统,该系统对无复发生存率(RFS)具有很高的判别能力。此外,该系统识别出的低复发风险癌细胞群显示了作为遗传表型的突变特征 SBS87。在此开发的基于人工智能的新型分类系统有望在肿瘤学的预后预测和个性化治疗选择中发挥重要作用。
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来源期刊
Modern Pathology
Modern Pathology 医学-病理学
CiteScore
14.30
自引率
2.70%
发文量
174
审稿时长
18 days
期刊介绍: Modern Pathology, an international journal under the ownership of The United States & Canadian Academy of Pathology (USCAP), serves as an authoritative platform for publishing top-tier clinical and translational research studies in pathology. Original manuscripts are the primary focus of Modern Pathology, complemented by impactful editorials, reviews, and practice guidelines covering all facets of precision diagnostics in human pathology. The journal's scope includes advancements in molecular diagnostics and genomic classifications of diseases, breakthroughs in immune-oncology, computational science, applied bioinformatics, and digital pathology.
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