In the field of theranostics, diagnostic nanoparticles are designed to collect highly patient-selective disease profiles, which is then leveraged by a set of nanotherapeutics to improve the therapeutic results. Despite their early promise, high interpatient and intratumoral heterogeneities make any rational design and analysis of these theranostics platforms extremely problematic. Recent advances in deep-learning-based tools may help bridge this gap, using pattern recognition algorithms for better diagnostic precision and therapeutic outcome. Triple-negative breast cancer (TNBC) is a conundrum because of the complex molecular diversity, making its diagnosis and therapy challenging. To address these challenges, we propose a method to predict the cellular internalization of nanoparticles (NPs) against different cancer stages using artificial intelligence. Here, we demonstrate for the first time that a combination of machine-learning (ML) algorithm and characteristic cellular uptake responses for individual cancer cell types can be successfully used to classify various cancer cell types. Utilizing this approach, we can optimize the nanomaterials to get an optimum structure–internalization response for a given particle. This methodology predicted the structure–internalization response of the evaluated nanoparticles with remarkable accuracy (Q² = 0.9). We anticipate that it can reduce the effort by minimizing the number of nanoparticles that need to be tested and could be utilized as a screening tool for designing nanotherapeutics. Following this, we have proposed a diagnostic nanomaterial-based platform used to assemble a patient-specific cancer profile with the assistance of machine learning (ML). The platform is composed of eight carbon nanoparticles (CNPs) with multifarious surface chemistries that can differentiate healthy breast cells from cancerous cells and then subclassify TNBC cells vs non-TNBC cells, within the TNBC group. The artificial neural network (ANN) algorithm has been successfully used in identifying the type of cancer cells from 36 unknown cancer samples with an overall accuracy of >98%, providing potential applications in cancer diagnostics.

中文翻译：

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精确的乳腺癌诊断和纳米细胞内在化预测的机器学习。

在肿瘤治疗学领域，诊断性纳米颗粒被设计用来收集高度患者选择性的疾病谱，然后由一组纳米治疗剂加以利用以改善治疗效果。尽管有早期的希望，但高的患者间和肿瘤内异质性使得对这些治疗学平台的任何合理设计和分析都极具问题。基于深度学习的工具的最新进展可能会使用模式识别算法提高诊断精度和治疗效果，从而帮助弥合这一差距。由于复杂的分子多样性，三阴性乳腺癌（TNBC）是一个难题。为了应对这些挑战，我们提出了一种使用人工智能来预测针对不同癌症阶段的纳米粒子（NPs）细胞内在化的方法。在这里，我们首次证明了针对单个癌细胞类型的机器学习（ML）算法和特征性细胞摄取反应的组合可以成功地用于对各种癌细胞类型进行分类。利用这种方法，我们可以优化纳米材料以获得给定粒子的最佳结构-内在化响应。该方法学以极高的准确性预测了所评估纳米颗粒的结构内在化响应（我们首次证明，针对单个癌细胞类型的机器学习（ML）算法和特征性细胞摄取反应的结合可以成功地用于对各种癌细胞类型进行分类。利用这种方法，我们可以优化纳米材料以获得给定粒子的最佳结构-内在化响应。该方法学以极高的准确性预测了所评估纳米颗粒的结构内在化响应（我们首次证明，针对单个癌细胞类型的机器学习（ML）算法和特征性细胞摄取反应的结合可以成功地用于对各种癌细胞类型进行分类。利用这种方法，我们可以优化纳米材料以获得给定粒子的最佳结构-内在化响应。该方法学以极高的准确性预测了所评估纳米颗粒的结构内在化响应（问²= 0.9）。我们预计它可以通过减少需要测试的纳米颗粒的数量来减少工作量，并且可以用作设计纳米治疗药物的筛选工具。在此之后，我们提出了一种基于诊断性纳米材料的平台，该平台可借助机器学习（ML）来组装特定于患者的癌症资料。该平台由具有多种表面化学性质的八个碳纳米粒子（CNP）组成，可以将健康的乳腺细胞与癌细胞区别开来，然后将TNBC细胞与非TNBC细胞进行分类。人工神经网络（ANN）算法已成功用于从36个未知癌症样本中识别癌细胞类型，总体准确度> 98％，为癌症诊断提供了潜在的应用。