Amin Ashwini, Priya Mallika, Rodrigues Jackson, Biswas Shimul, Chandra Subhash, Mathew Stanley, Ray Satadru, Rao Bola Sadashiva Satish, Mahato Krishna Kishore
Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India.
Department of Computer Science & Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India.
ACS Omega. 2025 May 14;10(20):20315-20325. doi: 10.1021/acsomega.4c11669. eCollection 2025 May 27.
Breast cancer poses a significant global health challenge, requiring improved diagnostic solutions for its timely intervention and treatment. Real-time diagnostic approaches in current practice offer promising avenues for early detection. However, these techniques often lack specificity, necessitating the development of robust diagnostic tools for real-time applications. In the current study, fluorescence spectroscopy is integrated with machine learning algorithms, and a graphical user interface (GUI) is developed for rapid breast cancer prediction. This study records 206 native fluorescence spectra, 103 spectra each from 31 normal and 31 malignant breast tissues using 325 nm excitation, followed by discrimination analysis using different machine learning algorithms, including backpropagation artificial neural network (BP-ANN), support vector machine (SVM), and Naïve Bayes (NB). Comparative analysis reveals that SVM in combination with a polynomial kernel demonstrated the superior performance of accuracy (98.78%), sensitivity (100%), specificity (97.56%), and precision (97.62%), among others. Furthermore, the in-house developed GUI applied to the current data showed the possibility of real-time prediction of pathological breast tissues, facilitating standalone applications.
乳腺癌是一项重大的全球健康挑战，需要改进诊断方法以便及时进行干预和治疗。当前实践中的实时诊断方法为早期检测提供了有前景的途径。然而，这些技术往往缺乏特异性，因此需要开发用于实时应用的强大诊断工具。在本研究中，将荧光光谱与机器学习算法相结合，并开发了一个图形用户界面（GUI）用于快速预测乳腺癌。本研究记录了206个原始荧光光谱，使用325nm激发光，分别从31个正常乳腺组织和31个恶性乳腺组织中各获取103个光谱，随后使用包括反向传播人工神经网络（BP-ANN）、支持向量机（SVM）和朴素贝叶斯（NB）在内的不同机器学习算法进行判别分析。对比分析表明，结合多项式核的支持向量机在准确率（98.78%）、灵敏度（100%）、特异性（97.56%）和精确率（97.62%）等方面表现优异。此外，将内部开发的GUI应用于当前数据显示了对乳腺病理组织进行实时预测的可能性，便于独立应用。
