The complexity of tumor biology is a multifaceted challenge that is governed by the intricate relationship among genetic mutations, epigenetic alterations, and the tumor microenvironment. Tumors are not static—they evolve through a series of genetic and epigenetic changes that enables them to evade the host’s immune system and resist the effects of various treatments. The tumor microenvironment, which comprises a diverse array of cell types, extracellular matrix components, and signaling molecules, significantly affect tumor growth, metastasis, and response to therapy. This renders it difficult to develop comprehensive treatment plans that can effectively target the specific characteristics of each tumor.

Optical microscopy imaging technologies have been adopted widely in precision oncology as they can address the challenges posed by the complexity of tumor biology. These technologies allow one to visualize and analyze tumor tissues and cells with high resolution, thus enabling quantitative and spatially localized analysis of genomic, proteomic, and metabolomic information. This level of detail is critical for identifying patient-specific molecular characteristics and biochemical abnormalities for developing targeted treatment strategies.

The significance of optical microscopy imaging in precision oncology is manifold. First, it bridges the difference between the genomic and phenotypic aspects of cancer, thus allowing for a more nuanced understanding of tumor behavior and response to therapy. Second, it enables the identification of biomarkers that can predict treatment response, thus providing guidance in selecting the most appropriate treatments for individual patients. Third, the non-invasive nature of these imaging techniques allows for the repeated monitoring of tumor progression and response to treatment, thereby facilitating real-time adjustments to treatment strategies as necessary.

The potential of optical microscopy imaging to transform cancer treatment is substantial. By providing detailed, patient-specific information, these imaging techniques can facilitate the development of more effective and less-toxic treatment regimens. This personalized approach can improve patient outcomes by increasing the efficacy of therapies and reducing the incidence of adverse effects. Furthermore, the ability to monitor treatment response in real time can facilitate more informed clinical decision-making, thus potentially improving the overall survival rates and quality of life of patients with cancer.

In conclusion, the integration of optical microscopy imaging into precision oncology is a significant advancement in cancer treatment. Optical microscopy imaging technologies are effective for understanding the complex biology of tumors and for guiding the development of personalized treatment strategies. As research in this field continues to progress, the potential for optical microscopy imaging to revolutionize cancer diagnosis and treatment will be immense, thus affording more targeted therapies and better patient outcomes in the future. The continued evolution of these technologies is crucial for bridging the disparity between genomic research and clinical practice, thus ultimately resulting in more effective and personalized cancer treatments.

Progress Optical microscopy imaging techniques have progressed significantly in the field of precision oncology and can provide a comprehensive view of tumor characteristics. Auto-fluorescence (AF) imaging has been utilized to monitor metabolic activities within tumors and offers label-free insights into drug responses and cellular metabolism (Fig.5). Second harmonic generation (SHG) imaging has been pivotal for analyzing the extracellular matrix (ECM), particularly collagen fiber organization, which is crucial for understanding tumor invasion and metastasis (Fig.7). Coherent Raman scattering (CRS), in particular stimulated Raman scattering (SRS), has emerged as an effective tool for imaging tumor metabolites without requiring labels. SRS has been instrumental in revealing metabolic heterogeneity, which is vital for identifying therapeutic targets and understanding cancer-cell metabolism (Fig.8). Mid-infrared photothermal (MIP) imaging has demonstrated its potential in assessing drug pharmacokinetics and pharmacodynamics by imaging the distribution of drugs within cells and tissues at a deep cellular level (Fig.9). Furthermore, multiplex immunofluorescence (mIF) and fluorescence insitu hybridization (FISH) have been employed for immunophenotyping (Fig.4) and genetic analysis (Fig.6), respectively, to characterize the immune microenvironment and detect gene amplifications. These techniques, as summarized in Table 1, collectively contribute to the increasing number of tools available for the characterization of tumors and the optimization of targeted therapies, thus ultimately improving patient outcomes in cancer treatment.

Conclusions and Prospects Optical microscopy imaging is becoming essential in precision oncology as it allows one to understand the relationship between tumor genetics and phenotypes. As the field progresses, the integration of these imaging techniques into clinical settings will become more evident, which will significantly improve cancer diagnostics and treatment. Future studies shall be conducted to render this technology more accessible by reducing equipment costs and enhancing imaging methodologies, thereby solidifying its key role in precision oncology.

Precision oncology is imperative for accommodating the distinct journey of each cancer patient, which is determined by the unique genetic, molecular, and cellular profiles of individual tumors. This shift from a general treatment model to a personalized approach is driven by the recognition that each patient with cancer presents a distinct set of challenges that must be addressed to achieve optimal therapeutic outcomes and prognostic accuracy. The conventional methods of cancer treatment, which typically involve generalized therapies, are deficient owing to the heterogeneity of tumors and the dynamic nature of cancer progression.

Progress We introduced a series of research efforts to advance the intraoperative application of optical coherence imaging. Vascular characteristics are an important basis for intraoperative pathological assessment. We first introduced OCT angiography with adaptive multi-time intervals, which proposes a time-efficient scanning protocol by adaptive optimization of the weights of different time-interval B-scan angiograms. This novel OCTA technique achieved better performance, with a visible vascular density increase of approximately 67% and a signal-to-noise ratio enhancement of approximately 11.6% (Figs. 2 and 3). In the context of intraoperative applications, we introduced robot-assisted OCTA, which integrated a high-resolution OCT system with a 6-degree of freedom robotic arm (Fig. 4). Robot-assisted OCTA can achieve wide-field imaging of artificially determined scanning paths. High-resolution vascular imaging of the mouse brain by robot-assisted OCTA successfully confirmed the effect of unevenly distributed resolution and fall-off caused by the large-curvature sample (Fig. 5). Thereafter, we introduced a microscope-integrated OCT system that can be well integrated with current intraoperative equipment and does not need to pause the surgical process (Figs. 6 and 7). Providing real-time tissue depth information to a doctor can help improve their decision-making ability in delicate surgical procedures such as ophthalmology and nervous system surgery. Intraoperative three-dimensional (3D) real-time imaging requires an OCT system with high imaging and processing speeds. Thereafter, we introduced the 10.3 MHz ultra-high speed scanning laser with stretch pulse mode-locked based on polarization isolation (Fig. 8), which employs a simple and low-cost approach to suppress the transmitted light and achieves an effective duty cycle of ～100% with only one CFBG and no need for intra-cavity semiconductor optical amplifier (SOA) modulation, extra-cavity optical buffering, and post amplification (Fig. 9). Real-time 3D OCT imaging is necessary for practical intraoperative applications, and a series of studies have been conducted to achieve this goal. A home-built 3.28 MHz FDML based OCT system combined with GPUs (NVIDIA, GeForce GTX690, and GeForce GTX680, USA) achieved real-time processing and visualization of 3D OCT data (Fig. 10). The imaging range and longitudinal resolution can be flexibly adjusted by changing the spectral range of the output.

Although OCT offers high-quality structural and vascular imaging, it lacks cellular resolution, which limits detailed tumor analysis. Dynamic full-field OCT (D-FFOCT) is an optically active rapid pathological imaging technology based on array interference detection that captures subcellular metabolic motion at millisecond temporal and nanometer spatial scales, and significantly enhances tumor diagnostics by providing detailed insights beyond conventional OCT capabilities (Fig. 11). Normal and diseased tissues can be accurately distinguished by analyzing the temporal characteristics of dynamic signals, such as amplitude, frequency, and standard difference. Through the use of high-power objective lenses and broadband light sources, the resolution can reach sub-microns, and as an imaging tool for intraoperative tissue sections, it is fast, easy (no freezing or staining is required), and highly accurate. Freshly isolated mouse brain glioma sections were imaged using the D-FFOCT system, which showed a clear boundary, distinct cell structure, and dynamic intensity between the glioma and normal brain tissue (Fig. 12).

Conclusions and Prospects Advancements in OCT technology, including the significantly increased sweep speed of the light source, improvement of the probe for the intraoperative scene, optimization of the blood flow algorithm, and high-speed data processing capability supported by the GPU, make real-time intraoperative 3D tomography possible. D-FFOCT imaging with a cell-resolving ability is an important step forward in the timely pathology of tumor resection. The integration of advanced OCT technologies into clinical practice heralds a new era of precision medicine in which surgical accuracy is significantly enhanced and tumor recurrence is minimized. Future studies should focus on further refining OCT capabilities, integrating these advanced technologies to improve clinical practicability, expanding their applications across different types of cancer, and integrating AI to automate and enhance diagnostic accuracy. This vision foresees OCT not only as a tool for improved surgical interventions but also as a pivotal element in the broader strategy of personalized and targeted treatment approaches, offering a beacon of hope for more effective cancer management and patient recovery paths. The ultimate goal is to establish OCT as an indispensable tool for tumor surgery and management, revolutionizing patient care and outcomes.

Optical coherence tomography (OCT) plays a pivotal role in medical imaging, particularly in enhancing the tumor resection accuracy. The significance of this technology lies in its ability to improve patient prognosis by providing real-time, detailed visualization of tumor boundaries and invasiveness, thereby reducing recurrence rates and aiding the precise removal of malignant tissues.

Wearable flexible electronics is one of the development trends in medical-health monitoring, particularly cardiovascular-disease monitoring. Pulse wave is an important source of information for assessing cardiovascular health; however, it is a non-stationary weak signal and imposes high requirements on the sensitivity and stability of detection. To solve the key technical problems of wearable health monitoring, a graphene-based flexible pressure sensor with a multilevel branched microstructure is designed and developed in this study, which significantly improves the sensing performance of pulse waves and forms the foundation for a wearable flexible pressure sensor. The sensing health-monitoring system is developed using a sensing-like cuffless blood-pressure-monitoring algorithm based on single-point radial artery pulse waves. The prediction errors of the system for human systolic blood pressure (SBP) and diastolic blood pressure (DBP) are (0.7±10.5) mmHg and (0.5±6.1) mmHg, respectively. The findings of this study can provide important technical support for cardiovascular health-monitoring systems and application research, as well as for wearable precision medical-health monitoring.

Flexible pressure sensors based on array structures exhibit key technical issues such as difficulty in achieving high sensitivity and a wide pressure-detection range, as well as limited usage. Hence, a hierarchical branch (HB) structure pressure-sensor design scheme is proposed in this study to improve the performance of array-microstructure pressure sensors. First, we completed the design of the HB structure via finite-element analysis. Results of the finite-element analysis reveal the unique effect of the HB structure: it not only includes the elastic-modulus- reduction effect caused by the superposition of multiple elastic layers but also integrates the pressure-diffusion effect caused by the HB structure, thus realizing the gradual activation and further strengthening of the active-layer conduction path. As such, the deformation range of the elastic layer (sensor pressure-detection range) and the deformation sensitivity to pressure (sensor sensitivity) can be improved. To solve the problem wherein the existing blood-pressure-detection equipment requires cuff pressurization and continuous blood-pressure monitoring cannot be achieved easily without pressurization interference, we construct a cuffless blood-pressure detection model, the class-aware model based on the Moens-Korteweg (M-K) equation and Transformers (the CAMKformer model). This model incorporates the idea of ??cascade learning, uses the basic formula for blood-pressure calculation based on pulse-wave conduction velocity as the principle, and applies the Transformer model to classify the input pulse wave for blood pressure, thus forming a two-stage cuffless blood-pressure detection model. Compared with conventional machine-learning algorithms based only on formulas or tree models, this model combines formula-related features with original pulse-wave data, where the complex feature-extraction capabilities of deep-learning models and the strong interpretability of theoretical models are fully utilized. In addition to affording high robustness, it integrates multimodal blood-pressure related information (discrete pulse-wave characteristics and continuous pulse-wave data), thus significantly reducing the blood-pressure detection errors inherent in conventional research methods.

Experimental results show that the HB structure enables flexible pressure sensors based on array microstructures to simultaneously improve sensitivity (an increase by 14 times, which is more significant than that of previously published single-layer structure strategies) and the linear range (Fig.6). Additionally, the HB structural strategy based on template and multilayer superposition methods offers significant advantages in terms of structural uniformity, adjustability, and scalability. For example, molds can be fabricated via highly controllable processes (such as photolithography), thus allowing parameters such as structural shape and size to be adjusted. We believe that the HB strategy can be used as a general strategy to adjust mechanical-stress transfer and optimize sensor performance, as well as exhibits broad application prospects in sensor design. A diverse database can further demonstrate the robustness and generalizability of the CAMKformer model. The results show that the wearable system and CAMKformer model constructed in this study can adapt promptly to the pulse-wave characteristics of different individuals and accurately detect human SBP and DBP [with errors of (0.7±10.5) mmHg and (0.5±6.1) mmHg, respectively, as shown in Table 3]. Different from the pressurized blood-pressure monitoring method of conventional electronic sphygmomanometers and commercial blood-pressure measurement smart watches, the abovementioned system does not require pressure application to the user’s radial artery to detect blood pressure; hence, it is suitable for continuously measuring the user’s blood pressure during daily activities or at night, as blood pressure changes during sleep. In addition, this model uses a single-cycle pulse wave as input, presents a simple system configuration, and is highly flexible for use.

In this study, wearable flexible electronic technology and medical-health monitoring are adopted as the research background. The requirements of wearable cardiovascular health monitoring are identified, and the associated principles, devices, systems, and application levels are investigated systematically. First, a design scheme for a HB structure flexible pressure sensor is proposed, and a graphene HB structure flexible pressure sensor is reconstructed simultaneously with a pulse-wave measurement system. Experimental results show that the bionic HB structure strategy enhances the sensitivity (an increase by 14 times) and linear range (4.6 times expansion) of the array-based microstructure pressure sensor, thus enabling distortion-free and accurate measurements of pulse waves. Subsequently, a class-aware cuffless blood-pressure-detection model is established. This model, which is based on the abovementioned flexible pressure sensor, obtains single-point pulse waves of the radial artery. Additionally, it uses a deep-learning model based on the Transformer for blood-pressure classification and a theoretical model based on the M-K equation for blood-pressure prediction. Compared with conventional machine-learning algorithms based only on formulas or tree models, this algorithm combines commonly used pulse features with original pulse-wave data, where the complex feature-extraction capabilities of deep-learning models and the strong interpretability and robustness of theoretical models are fully exploited. Owing to its high stickiness, it realizes the fusion of multimodal pulse-related information and significantly reduces the error of cuffless blood-pressure detection [the errors are (0.7±10.5) mmHg and (0.5±6.1) mmHg for SBP and DBP, respectively], thus satisfying the international standards for non-invasive blood-pressure monitors. The cuffless blood-pressure monitoring system proposed herein is devoid of external pressure interference. Additionally, it is expected to transform the blood-pressure dynamic detection mode from “single-point, high-dispersion transient detection” to “multipoint, low-dispersion online monitoring,” thus facilitating users or doctors in dynamically monitoring blood-pressure changes to achieve early proactive screening of hypertension and improve hypertension early-warning and monitoring capabilities.