Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learning

TL;DR

This paper introduces a tensor-network machine learning approach for non-invasive lung cancer screening using Raman spectra data, emphasizing interpretability and high prediction certainty, which enhances reliability in clinical diagnostics.

Contribution

The study presents a novel tensor-network ML method that provides interpretable, highly accurate predictions of lung cancer stages from breath spectra, with quantitative certainty measures.

Findings

Prediction accuracy nearly 100% for high-certainty samples

Incorrect predictions have lower certainty, enabling anomaly detection

Shifts AI in biomedical sciences towards interpretable, reliable models

Abstract

Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from academic researches to clinical applications, such as in biomarkers' detection and diagnosis, optimization of treatment, and identification of new therapeutic targets in drug discovery. However, the contemporary AI technologies, particularly deep machine learning (ML), severely suffer from non-interpretability, which might uncontrollably lead to incorrect predictions. Interpretability is particularly crucial to ML for clinical diagnosis as the consumers must gain necessary sense of security and trust from firm grounds or convincing interpretations. In this work, we propose a tensor-network (TN)-ML method to reliably predict lung cancer patients and their stages via screening Raman spectra data of Volatile organic compounds (VOCs) in exhaled breath, which are generally suitable as biomarkers and are considered to be an ideal way for non-invasive lung cancer screening. The prediction of TN-ML is based on the mutual distances of the breath samples mapped to the quantum Hilbert space. Thanks to the quantum probabilistic interpretation, the certainty of the predictions can be quantitatively characterized. The accuracy of the samples with high certainty is almost 100$\%$. The incorrectly-classified samples exhibit obviously lower certainty, and thus can be decipherably identified as anomalies, which will be handled by human experts to guarantee high reliability. Our work sheds light on shifting the ``AI for biomedical sciences'' from the conventional non-interpretable ML schemes to the interpretable human-ML interactive approaches, for the purpose of high accuracy and reliability.