I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Bari, Italy.
PLoS One. 2024 Nov 21;19(11):e0312036. doi: 10.1371/journal.pone.0312036. eCollection 2024.
Detecting patients at high risk of occurrence of an Invasive Disease Event after a first diagnosis of breast cancer, such as recurrence, distant metastasis, contralateral tumor and second tumor, could support clinical decision-making processes in the treatment of this malignancy. Though several machine learning models analyzing both clinical and histopathological information have been developed in literature to address this task, these approaches turned out to be unsuitable for describing this problem.
In this study, we designed a novel artificial intelligence-based approach which converts clinical information into an image-form to be analyzed through Convolutional Neural Networks. Specifically, we predicted the occurrence of an Invasive Disease Event at both 5-year and 10-year follow-ups of 696 female patients with a first invasive breast cancer diagnosis enrolled at IRCCS "Giovanni Paolo II" in Bari, Italy. After transforming each patient, represented by a vector of clinical information, to an image form, we extracted low-level quantitative imaging features by means of a pre-trained Convolutional Neural Network, namely, AlexNET. Then, we classified breast cancer patients in the two classes, namely, Invasive Disease Event and non-Invasive Disease Event, via a Support Vector Machine classifier trained on a subset of significative features previously identified.
Both 5-year and 10-year models resulted particularly accurate in predicting breast cancer recurrence event, achieving an AUC value of 92.07% and 92.84%, an accuracy of 88.71% and 88.82%, a sensitivity of 86.83% and 88.06%, a specificity of 89.55% and 89.3%, a precision of 71.93% and 84.82%, respectively.
This is the first study proposing an approach which converts clinical information into an image-form to develop a decision support system for identifying patients at high risk of occurrence of an Invasive Disease Event, and then defining personalized oncological therapeutic treatments for breast cancer patients.
检测首次诊断为乳腺癌后发生侵袭性疾病事件（如复发、远处转移、对侧肿瘤和第二肿瘤）风险较高的患者，可为这种恶性肿瘤的治疗提供临床决策支持。尽管文献中已经开发了几种分析临床和组织病理学信息的机器学习模型来解决这个问题，但这些方法并不适用于描述这个问题。
在这项研究中，我们设计了一种新的基于人工智能的方法，将临床信息转换为图像形式，通过卷积神经网络进行分析。具体来说，我们预测了意大利巴里的 IRCCS“Giovanni Paolo II”收治的 696 名首次浸润性乳腺癌女性患者在 5 年和 10 年随访期间发生侵袭性疾病事件的情况。将每个患者（由临床信息向量表示）转换为图像形式后，我们使用预先训练的卷积神经网络（即 AlexNET）提取低水平的定量成像特征。然后，我们通过在之前确定的有意义的特征子集上训练的支持向量机分类器将乳腺癌患者分为侵袭性疾病事件和非侵袭性疾病事件两类。
5 年和 10 年模型在预测乳腺癌复发事件方面均表现出较高的准确性，AUC 值分别为 92.07%和 92.84%，准确率分别为 88.71%和 88.82%，灵敏度分别为 86.83%和 88.06%，特异性分别为 89.55%和 89.3%，精确率分别为 71.93%和 84.82%。
这是第一项提出将临床信息转换为图像形式的方法的研究，旨在开发一种决策支持系统，以识别发生侵袭性疾病事件风险较高的患者，然后为乳腺癌患者制定个性化的肿瘤治疗方案。
Zotero 插件
