Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis

Abstract

Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy.

Keywords: biosensors; cancer detection; impedance cytometry; lab-on-a-chip; machine learning; microfluidic chips.

Figure 1

Schematic illustrations of supervised machine learning algorithms. (A) SVM model. Reprinted from [52]. (B) KNN model Reprinted from [53]. (C) DT model. Reprinted from [54]. (D) LR model Reprinted from [55]. (E) ANN model. Reprinted from [56].

Figure 2

(A) Cross-sectional view of the proposed SPR sensor with the experimental setup. Reprinted with permission from [64]. Copyright 2023 Elsevier. (B) Colored entities of the designed sensor with a cross-sectional view with the experimental setup. Reprinted with permission from [65]. Copyright 2023 IEEE. (C) Schemes of the digital in-line holographic microscope (DIHM). Reprinted with permission from [68]. Copyright 2023 Springer Nature.

Figure 8

(A) Multifrequency impedance cytometry measures the response across a broad range of frequencies to assess cellular responses to a target drug. Machine learning algorithms are utilized to predict the viability of both live and dead cells. Reprinted with permission from [105]. Copyright 2021 Springer Nature. (B) Graphical representation illustrating the concept of breath biopsy. Breast cancer cells produce volatile metabolites that travel to the lungs and are exhaled. By using a sensor array to analyze these biomarkers in the breath, we can identify the molecular subtype of breast cancer at an early stage. Reprinted from [107]. (C) The proposed breast cancer detection system is a Smart Bra. Reprinted with permission from [108]. Copyright 2020 John Wiley and Sons. (D) The ML-assisted biochip performs single-cell classification in a label-free manner. The machine learning algorithm is used to perform both cell health classification (cancerous vs. non-cancerous) and cancer subtype cell discrimination at the single cell level. Reprinted with the permission from [109]. Copyright 2020 John Wiley and Sons.

Figure 3

(A) Overview of the combined microscopic cell imaging and deep learning approach. Reprinted from [70]. Copyright 2021 Springer Nature. (B) Schematic of the biosensor with the combination of machine learning methods to detect lung cancers. Reprinted from [72]. (C) Schematic of the experimental setups. Reprinted with permission from [73]. Copyright 2018 John Wiley and Sons.

Figure 4

Schematic diagram of lung cancer diagnosis by SERS classification of the exosome. (A,B) Lung cancer cells and normal cells release exosomes to the extracellular environment, having their own profiles by fusing multivesicular endosomes to the plasma membrane, respectively. (C,D) Raman spectra of lung cancer cells and normal cell-derived exosomes were achieved by SERS, respectively. (E) SERS spectra, achieved by methods of panels (C,D), are shown. Red lines indicate specific peaks of lung cancer-derived exosomes. (F) Exosome classification is obtained by PCA of SERS spectra. Reprinted with permission from [77]. Copyright 2017 American Chemical Society.

Figure 5

The schematic diagram of high-content VFC and the 3D schematic diagram of the sheath flow in the flow chamber. Reprinted with permission from [87]. Copyright 2022 John Wiley and Sons.

Figure 6

The schematic shows an overview of the dynamic morphological analysis of cell gestures. Reprinted with permission from [94]. Copyright 2018 Elsevier.

Figure 7

Schematic diagram of an electrical impedance cytometer. As cells flow through microfluidic chips, the change in impedance is measured by a lock-in amplifier. The lock-in amplifier can apply signals in different frequencies at a time. The data is then recorded and analyzed using the ANN algorithm.

Figure 9

Overview of e-nose biosensor for liver cancer detection from VOCs in breath. Reprinted with permission from [114]. Copyright 2023 Elsevier.