Perspective on quantitative phase imaging to improve precision cancer medicine

Abstract

Significance: Quantitative phase imaging (QPI) offers a label-free approach to non-invasively characterize cellular processes by exploiting their refractive index based intrinsic contrast. QPI captures this contrast by translating refractive index associated phase shifts into intensity-based quantifiable data with nanoscale sensitivity. It holds significant potential for advancing precision cancer medicine by providing quantitative characterization of the biophysical properties of cells and tissue in their natural states.

Aim: This perspective aims to discuss the potential of QPI to increase our understanding of cancer development and its response to therapeutics. It also explores new developments in QPI methods towards advancing personalized cancer therapy and early detection.

Approach: We begin by detailing the technical advancements of QPI, examining its implementations across transmission and reflection geometries and phase retrieval methods, both interferometric and non-interferometric. The focus then shifts to QPI's applications in cancer research, including dynamic cell mass imaging for drug response assessment, cancer risk stratification, and in-vivo tissue imaging.

Results: QPI has emerged as a crucial tool in precision cancer medicine, offering insights into tumor biology and treatment efficacy. Its sensitivity to detecting nanoscale changes holds promise for enhancing cancer diagnostics, risk assessment, and prognostication. The future of QPI is envisioned in its integration with artificial intelligence, morpho-dynamics, and spatial biology, broadening its impact in cancer research.

Conclusions: QPI presents significant potential in advancing precision cancer medicine and redefining our approach to cancer diagnosis, monitoring, and treatment. Future directions include harnessing high-throughput dynamic imaging, 3D QPI for realistic tumor models, and combining artificial intelligence with multi-omics data to extend QPI's capabilities. As a result, QPI stands at the forefront of cancer research and clinical application in cancer care.

Keywords: cancer research; computational imaging; interferometry; quantitative phase imaging.

Fig. 1

Example of light path in (a) transmission phase imaging and (b-d) three different scenarios in reflection phase imaging configurations, including (b) double-transmission case with a strong reflective substrate, (c) a case of strong multi-layer reflections at interfaces with a large refractive index mismatch, and (d) a case of weakly-varying refractive indices within the sample where the reflection from the reflective surface serves as reference wave interferes with the weak backscattered light from heterogenous refractive index changes within the sample.

Fig. 2

Frequency domain support of QPI modalities. (a) Transmission and reflection mode QPI. In the reflection mode, the spectral bandwidth of the light source provides axial ($K_z$) frequency support resulting in its ability to perform depth-resolved QPI. In contrast, this frequency support collapses to the origin in the transmission mode with $K_z=0$ for all source wavelengths. As a result, the transmission mode does not perform depth-resolved QPI but is able to capture the average quantitative phase with its frequency support centered around the origin. Reflection mode is unable to capture the latter due to a lack of frequency support at the origin. Instead, it captures higher frequency structures from the sample due to its frequency support away from the origin. Both provide similar, numerical aperture-dependent lateral resolution, as indicated by the lateral spread of the frequency support. (b) Optical diffraction tomography. Missing cone in ODT frequency support reduces axial resolution of ODT based 3D QPI imaging.

Fig. 3

Representative images from (a) transmission QPI of cells embedded in resin-based substrates, (b) reflection QPI of a USAF target in the presence of strong interfaces, and (c) and (d) reflection QPI of weakly scattering objects (cells embedded in resin-based substrates) in the absence of strong interfaces at different depths.

Fig. 4

Examples for the applications of QPI in assessing drug response. (a) The fold change of average cell dry mass in MCF-7 cells over 72 h of treatment with $20\mu \mathrm{M}$ doxorubicin (cytotoxic response, slow decrease in dry mass) and $20\mu \mathrm{M}$ fulvestrant (cytostatic response, gradual increase in dry mass) in comparison with the control (DMSO), adapted with permission from Ref. . (b) Temporal changes of cell dry mass in macrophages (RAW 264.7) treated with different concentrations of lipid-based nanoparticles (LipImage 815), compared to cytotoxicity and medium controls. The results suggest LipImage 815 as low-toxic. The figure is adapted from Ref. . (c) The 3D refractive index maps in cells undergoing regular proliferation (control) and senescent cells. Senescent cells showed more lipid droplet accumulation compared to controls (those with the highest refractive index). The figure is adapted with permission from Ref. .

Fig. 5

Examples for the applications of QPI for quantitative diagnosis and precision prevention. (a) Transmission quantitative phase images for urine cytology samples (negative, atypical, suspicious, and positive classified by an expert cytopathologist). (b) The average nuclear dry mass for each patient group. (c)–(f) Application of reflection QPI for assessing 3D nanoscale nuclear architecture for cancer risk stratification in patients with ulcerative colitis. The figures were adapted from Ref. . (c) and (d) 3D-nanoNAM from a low-risk and a high-risk patient. (e) The 3D-nanoNAM derived properties projected onto a unit 2-sphere, where most of the low- and high-risk patients lie on two separate hemispheres. (f) The area under receiving operating curve (ROC) for distinguishing the low- from high-risk patients is $0.87\pm 0.04$. The figures were adapted from Ref. .

Fig. 6

Conceptual framework for bridging dynamic QPI imaging of cell morphology to improve precision cancer medicine. (a)  Dynamic morphological changes of cellular and subcellular structures over an extended period, as shown in this representative image sequence which illustrates the plastic nature of cell morphology in cells, coupled with (b) spatial multi-omics performed on the same cell population, and (c) assisted with AI and computational modeling, have the potential to predict cell fate decisions.