Imaging and cancer: a review

Label-free, real-time monitoring of cytochrome C drug responses in microdissected tumor biopsies with a multi-well aptasensor platform

Functional assays on intact tumor biopsies can complement genomics-based approaches for precision oncology, drug testing, and organs-on-chips cancer disease models by capturing key therapeutic response determinants, such as tissue architecture, tumor heterogeneity, and the tumor microenvironment. Most of these assays rely on fluorescent labeling, a semiquantitative method best suited for single-time-point assays or labor-intensive immunostaining analysis. Here, we report integrated aptamer electrochemical sensors for on-chip, real-time monitoring of cytochrome C, a cell death indicator, from intact microdissected tissues with high affinity and specificity. The platform features a multi-well sensor layout and a multiplexed electronic setup. The aptasensors measure increases in cytochrome C in the supernatant of mouse or human microdissected tumors after exposure to various drug treatments. Because of the sensor's high affinity, it primarily tracks rising concentrations of cytochrome C, capturing dynamic changes during apoptosis. This approach could help develop more advanced cancer disease models and apply to other complex in vitro disease models, such as organs-on-chips and organoids.

A systematic literature analysis of multi-organ cancer diagnosis using deep learning techniques

Cancer is becoming the most toxic ailment identified among individuals worldwide. The mortality rate has been increasing rapidly every year, which causes progression in the various diagnostic technologies to handle this illness. The manual procedure for segmentation and classification with a large set of data modalities can be a challenging task. Therefore, a crucial requirement is to significantly develop the computer-assisted diagnostic system intended for the initial cancer identification. This article offers a systematic review of Deep Learning approaches using various image modalities to detect multi-organ cancers from 2012 to 2023. It emphasizes the detection of five supreme predominant tumors, i.e., breast, brain, lung, skin, and liver. Extensive review has been carried out by collecting research and conference articles and book chapters from reputed international databases, i.e., Springer Link, IEEE Xplore, Science Direct, PubMed, and Wiley that fulfill the criteria for quality evaluation. This systematic review summarizes the overview of convolutional neural network model architectures and datasets used for identifying and classifying the diverse categories of cancer. This study accomplishes an inclusive idea of ensemble deep learning models that have achieved better evaluation results for classifying the different images into cancer or healthy cases. This paper will provide a broad understanding to the research scientists within the domain of medical imaging procedures of which deep learning technique perform best over which type of dataset, extraction of features, different confrontations, and their anticipated solutions for the complex problems. Lastly, some challenges and issues which control the health emergency have been discussed.

Next-generation chemotherapy treatments based on black hole algorithms: From cancer remission to chronic disease management

PROBLEM

Therapeutic planning strategies have been developed to enhance the effectiveness of cancer drugs. Nevertheless, their performance is highly limited by the inefficient biological representativeness of predictive tumor growth models, which hinders their translation to clinical practice.

OBJECTIVE

This study proposes a disruptive approach to oncology based on nature-inspired control using realistic Black Hole physical laws, in which tumor masses are trapped to experience attraction dynamics on their path to complete remission or to become a chronic disease. This control method is designed to operate independently of individual patient idiosyncrasies, including high tumor heterogeneities and highly uncertain tumor dynamics, making it a promising avenue for advancing beyond the limitations of the traditional survival probabilistic paradigm.

DESIGN

Here, we provide a multifaceted study of chemotherapy therapeutic planning that includes: (1) the design of a pioneering controller algorithm based on physical laws found in the Black Holes; (2) investigation of the ability of this controller algorithm to ensure stable equilibrium treatments; and (3) simulation tests concerning tumor volume dynamics using drugs with significantly different pharmacokinetics (Cyclophosphamide and Atezolizumab), tumor volumes (200 mm3 and 12 732 mm3) and modeling characterizations (Gompertzian and Logistic tumor growth models).

RESULTS

Our results highlight the ability of this new astrophysical-inspired control algorithm to perform effective chemotherapy treatments for multiple tumor-treatment scenarios, including tumor resistance to chemotherapy, clinical scenarios modelled by time-dependent parameters, and highly uncertain tumor dynamics.

CONCLUSIONS

Our findings provide strong evidence that cancer therapy inspired by phenomena found in black holes can emerge as a disruptive paradigm. This opens new high-impacting research directions, exploring synergies between astrophysical-inspired control algorithms and Artificial Intelligence applied to advanced personalized cancer therapeutics.

Synthesis and evaluation of curcumin reduced and capped gold nanoparticles as a green diagnostic probe with therapeutic potential

Curcumin, a compound in turmeric, shows promise for its anti-cancer properties. In this study, we successfully synthesised curcumin-reduced and capped gold nanoparticles. Most evaluations have been limited to in-vitro studies for these nanoparticles; our study takes a step further by highlighting the in-vivo assessment of these curcumin-reduced and capped gold nanoparticles (GNPCs) using non-invasive imaging (SPECT and optical) and possible therapeutic potential. The GNPCs showed an average hydrodynamic diameter of 58 nm and a PDI of 0.336. The synthesised and fully characterised GNPCs showed ex-vivo hemolysis value of ≤ 1.74 % and serum stability of ≥ 95 % over 24 h. Using in-vivo non-invasive (SPECT and optical Imaging), prolonged circulation and enhanced bioavailability of GNPCs were seen. The biodistribution studies after radiolabelling GNPCs with 99 mTc complemented the optical imaging. The SPECT images showed higher uptake of the GNPCs at the tumour site, viz the contralateral muscle and the native Curcumin, resulting in a high target-to-non-target ratio that differentiated the tumour sufficiently and enhanced the diagnostics. Other organs also accumulate radiolabeled GNPCs in systemic circulation; bio dosimetry is performed. It was found that the dose received by the different organs was safe for use, and the in-vivo toxicity studies in rats indicated negligible toxicity over 30 days. The tumour growth was also reduced in mice models treated with GNPCs compared to the control. These significant findings demonstrate that GNPC shows synergistic activity in vivo, indicating its ability as a green diagnostic probe that has the potential for therapy.

Recent advances in minimally invasive biomarkers of OSCC: from generalized to personalized approach

Oral cancer is the 6th most common type of cancer worldwide, and oral squamous cell carcinoma (OSCC) accounts for >90% of oral cancers. It is a major health problem, particularly in low- and middle-income countries (LMICs), due to both its high incidence and significant mortality and morbidity. Despite being a global burden, and even with the significant advancement in the management of OSCC, the overall outcome of the disease is still abysmal. With the advent of time, advanced diagnostic and treatment approaches have come into practice, but the burden of the disease has not improved significantly. Major reasons attributed to the poor outcome are delay in diagnosis, locoregional recurrence and resistance to the currently available treatment regimen. In this review, we have highlighted the existing challenges in the diagnosis and have emphasized the advancements in minimally invasive biomarkers. Additionally, the importance of collaborative multidimensional approaches involving clinicians and researchers has been discussed, as well as the need to redefine and establish better utility and management of existing diagnostic and treatment protocols along with the minimally invasive/non-invasive biomarkers.

Radiomics for clinical decision support in radiation oncology

Radiomics is a promising tool for the development of quantitative biomarkers to support clinical decision-making. It has been shown to improve the prediction of response to treatment and outcome in different settings, particularly in the field of radiation oncology by optimising the dose delivery solutions and reducing the rate of radiation-induced side effects, leading to a fully personalised approach. Despite the promising results offered by radiomics at each of these stages, standardised methodologies, reproducibility and interpretability of results are still lacking, limiting the potential clinical impact of these tools. In this review, we briefly describe the principles of radiomics and the most relevant applications of radiomics at each stage of cancer management in the framework of radiation oncology. Furthermore, the integration of radiomics into clinical decision support systems is analysed, defining the challenges and offering possible solutions for translating radiomics into a clinically applicable tool.

Recent Advances in Biosensor Technology for Early-Stage Detection of Hepatocellular Carcinoma-Specific Biomarkers: An Overview

Hepatocellular carcinoma is currently the most common malignancy of the liver. It typically occurs due to a series of oncogenic mutations that lead to aberrant cell replication. Most commonly, hepatocellular carcinoma (HCC) occurs as a result of pre-occurring liver diseases, such as hepatitis and cirrhosis. Given its aggressive nature and poor prognosis, the early screening and diagnosis of HCC are crucial. However, due to its plethora of underlying risk factors and pathophysiologies, patient presentation often varies in the early stages, with many patients presenting with few, if any, specific symptoms in the early stages. Conventionally, screening and diagnosis are performed through radiological examination, with diagnosis confirmed by biopsy. Imaging modalities tend to be limited by their requirement of large, expensive equipment; time-consuming operation; and a lack of accurate diagnosis, whereas a biopsy's invasive nature makes it unappealing for repetitive use. Recently, biosensors have gained attention for their potential to detect numerous conditions rapidly, cheaply, accurately, and without complex equipment and training. Through their sensing platforms, they aim to detect various biomarkers, such as nucleic acids, proteins, and even whole cells extracted by a liquid biopsy. Numerous biosensors have been developed that may detect HCC in its early stages. We discuss the recent updates in biosensing technology, highlighting its competitive potential compared to conventional methodology and its prospects as a tool for screening and diagnosis.

Developing a Novel Image Marker to Predict the Clinical Outcome of Neoadjuvant Chemotherapy (NACT) for Ovarian Cancer Patients

Objective

Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the clinical outcome to NACT vary significantly among different subgroups. The patients with partial responses to NACT may lead to suboptimal debulking surgery, which will result in adverse prognosis. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy prognosis prediction of the NACT at an early stage.

Methods

For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. This cluster was used as input for developing and optimizing support vector machine (SVM) based classifiers, which indicated the likelihood of the patient receiving suboptimal cytoreduction after the NACT treatment. Two different kernels for SVM algorithm were explored and compared. To validate this scheme, a total of 42 ovarian cancer patients were retrospectively collected. A nested leave-one-out cross-validation framework was adopted for model performance assessment.

Results

The results demonstrated that the model with a Gaussian radial basis function kernel SVM yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.806 ± 0.078. Meanwhile, this model achieved overall accuracy (ACC) of 83.3%, positive predictive value (PPV) of 81.8%, and negative predictive value (NPV) of 83.9%.

Conclusion

This study provides meaningful information for the development of radiomics based image markers in NACT treatment outcome prediction.

A radiograph-based deep learning model improves radiologists' performance for classification of histological types of primary bone tumors: A multicenter study

PURPOSE

To develop a deep learning (DL) model for classifying histological types of primary bone tumors (PBTs) using radiographs and evaluate its clinical utility in assisting radiologists.

METHODS

This retrospective study included 878 patients with pathologically confirmed PBTs from two centers (638, 77, 80, and 83 for the training, validation, internal test, and external test sets, respectively). We classified PBTs into five categories by histological types: chondrogenic tumors, osteogenic tumors, osteoclastic giant cell-rich tumors, other mesenchymal tumors of bone, or other histological types of PBTs. A DL model combining radiographs and clinical features based on the EfficientNet-B3 was developed for five-category classification. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate model performance. The clinical utility of the model was evaluated in an observer study with four radiologists.

RESULTS

The combined model achieved a macro average AUC of 0.904/0.873, with an accuracy of 67.5 %/68.7 %, a macro average sensitivity of 66.9 %/57.2 %, and a macro average specificity of 92.1 %/91.6 % on the internal/external test set, respectively. Model-assisted analysis improved accuracy, interpretation time, and confidence for junior (50.6 % vs. 72.3 %, 53.07[s] vs. 18.55[s] and 3.10 vs. 3.73 on a 5-point Likert scale [P < 0.05 for each], respectively) and senior radiologists (68.7 % vs. 75.3 %, 32.50[s] vs. 21.42[s] and 4.19 vs. 4.37 [P < 0.05 for each], respectively).

CONCLUSION

The combined DL model effectively classified histological types of PBTs and assisted radiologists in achieving better classification results than their independent visual assessment.

Tumor-specific enhanced NIR-II photoacoustic imaging via photothermal and low-pH coactivated AuNR@PNIPAM-VAA nanogel

BACKGROUND

Properly designed second near-infrared (NIR-II) nanoplatform that is responsive tumor microenvironment can intelligently distinguish between normal and cancerous tissues to achieve better targeting efficiency. Conventional photoacoustic nanoprobes are always "on", and tumor microenvironment-responsive nanoprobe can minimize the influence of endogenous chromophore background signals. Therefore, the development of nanoprobe that can respond to internal tumor microenvironment and external stimulus shows great application potential for the photoacoustic diagnosis of tumor.

RESULTS

In this work, a low-pH-triggered thermal-responsive volume phase transition nanogel gold nanorod@poly(n-isopropylacrylamide)-vinyl acetic acid (AuNR@PNIPAM-VAA) was constructed for photoacoustic detection of tumor. Via an external near-infrared photothermal switch, the absorption of AuNR@PNIPAM-VAA nanogel in the tumor microenvironment can be dynamically regulated, so that AuNR@PNIPAM-VAA nanogel produces switchable photoacoustic signals in the NIR-II window for tumor-specific enhanced photoacoustic imaging. In vitro results show that at pH 5.8, the absorption and photoacoustic signal amplitude of AuNR@PNIPAM-VAA nanogel in NIR-II increases up obviously after photothermal modulating, while they remain slightly change at pH 7.4. Quantitative calculation presents that photoacoustic signal amplitude of AuNR@PNIPAM-VAA nanogel at 1064 nm has ~ 1.6 folds enhancement as temperature increases from 37.5 °C to 45 °C in simulative tumor microenvironment. In vivo results show that the prepared AuNR@PNIPAM-VAA nanogel can achieve enhanced NIR-II photoacoustic imaging for selective tumor detection through dynamically responding to thermal field, which can be precisely controlled by external light.

CONCLUSIONS

This work will offer a viable strategy for the tumor-specific photoacoustic imaging using NIR light to regulate the thermal field and target the low pH tumor microenvironment, which is expected to realize accurate and dynamic monitoring of tumor diagnosis and treatment.

The Deep Learning Framework iCanTCR Enables Early Cancer Detection Using the T-cell Receptor Repertoire in Peripheral Blood

T cells recognize tumor antigens and initiate an anticancer immune response in the very early stages of tumor development, and the antigen specificity of T cells is determined by the T-cell receptor (TCR). Therefore, monitoring changes in the TCR repertoire in peripheral blood may offer a strategy to detect various cancers at a relatively early stage. Here, we developed the deep learning framework iCanTCR to identify patients with cancer based on the TCR repertoire. The iCanTCR framework uses TCRβ sequences from an individual as an input and outputs the predicted cancer probability. The model was trained on over 2,000 publicly available TCR repertoires from 11 types of cancer and healthy controls. Analysis of several additional publicly available datasets validated the ability of iCanTCR to distinguish patients with cancer from noncancer individuals and demonstrated the capability of iCanTCR for the accurate classification of multiple cancers. Importantly, iCanTCR precisely identified individuals with early-stage cancer with an AUC of 86%. Altogether, this work provides a liquid biopsy approach to capture immune signals from peripheral blood for noninvasive cancer diagnosis.

SIGNIFICANCE

Development of a deep learning-based method for multicancer detection using the TCR repertoire in the peripheral blood establishes the potential of evaluating circulating immune signals for noninvasive early cancer detection.

Decorrelation Time Mapping as an Analysis Tool for Nanobubble-Based Contrast Enhanced Ultrasound Imaging

Nanobubbles (NBs; ~100-500 nm diameter) are preclinical ultrasound (US) contrast agents that expand applications of contrast enhanced US (CEUS). Due to their sub-micron size, high particle density, and deformable shell, NBs in pathological states of heightened vascular permeability (e.g. in tumors) extravasate, enabling applications not possible with microbubbles (~1000-10,000 nm diameter). A method that can separate intravascular versus extravascular NB signal is needed as an imaging biomarker for improved tumor detection. We present a demonstration of decorrelation time (DT) mapping for enhanced tumor NB-CEUS imaging. In vitro models validated the sensitivity of DT to agent motion. Prostate cancer mouse models validated in vivo imaging potential and sensitivity to cancerous tissue. Our findings show that DT is inversely related to NB motion, offering enhanced detail of NB dynamics in tumors, and highlighting the heterogeneity of the tumor environment. Average DT was high in tumor regions (~9 s) compared to surrounding normal tissue (~1 s) with higher sensitivity to tumor tissue compared to other mapping techniques. Molecular NB targeting to tumors further extended DT (11 s) over non-targeted NBs (6 s), demonstrating sensitivity to NB adherence. From DT mapping of in vivo NB dynamics we demonstrate the heterogeneity of tumor tissue while quantifying extravascular NB kinetics and delineating intra-tumoral vasculature. This new NB-CEUS-based biomarker can be powerful in molecular US imaging, with improved sensitivity and specificity to diseased tissue and potential for use as an estimator of vascular permeability and the enhanced permeability and retention (EPR) effect in tumors.

In Vivo and Post-mortem Comparisons of IVIM/Time-dependent Diffusion MR Imaging Parameters in Melanoma and Breast Cancer Xenograft Models

PURPOSE

We aimed to investigate the changes in intravoxel incoherent motion (IVIM) and diffusion parameters between in vivo and post-mortem conditions and the time dependency of these parameters using two different mouse tumor models with different vessel lumen sizes.

METHODS

Six B16 and six MDA-MB-231 xenograft mice were scanned using 7 Tesla MRI under both in vivo/post-mortem conditions. Diffusion weighted imaging with 17 b-values (0-3000 s/mm2) were obtained at two diffusion times (9 and 27.6 ms). The shifted apparent diffusion coefficient (sADC) using 2 b-values (200 and 1500 s/mm2), non-Gaussian diffusion and IVIM parameters (ADC0, K, fIVIM) were estimated at each of the diffusion times. The results were evaluated by repeated measures two-way analysis of variance and post hoc Bonferroni test.

RESULTS

In B16 tumors, fIVIM significantly decreased with post-mortem conditions (from 12.6 ± 6.5% to 5.2 ± 1.9%, P < 0.05 at long diffusion time; from 11.0 ± 2.4% to 4.6 ± 2.7%, P < 0.05 at short diffusion time). In MDA-MB-231 tumors, fIVIM also significantly decreased (from 8.8 ± 3.8% to 2.6 ± 1.1%, P < 0.05 at long; from 7.9 ± 5.4% to 2.9 ± 1.1%, P < 0.05 at short). No diffusion time dependency was observed (P = 0.59 in B16 and P = 0.77 in MDA-MB-231). The sADC and ADC0 values tended to decrease and the K value tended to increase after sacrificing and when increasing the diffusion time.

CONCLUSION

The fIVIM values dropped after sacrificing, confirming that IVIM MRI is a promising quantitative parameter to evaluate blood microcirculation. The presence of residual post-mortem fIVIM values suggested that the influence of water molecule diffusion in the blood lumen may contribute to the IVIM effect. Diffusion MRI parameter's time dependency and those changes after sacrificing could possibly provide additional insights into diffusion hindrance mechanisms.

Tumor versus Tumor Cell Targeting in Metal-Based Nanoparticles for Cancer Theranostics

The application of metal-based nanoparticles (mNPs) in cancer therapy and diagnostics (theranostics) has been a hot research topic since the early days of nanotechnology, becoming even more relevant in recent years. However, the clinical translation of this technology has been notably poor, with one of the main reasons being a lack of understanding of the disease and conceptual errors in the design of mNPs. Strikingly, throughout the reported studies to date on in vivo experiments, the concepts of "tumor targeting" and "tumor cell targeting" are often intertwined, particularly in the context of active targeting. These misconceptions may lead to design flaws, resulting in failed theranostic strategies. In the context of mNPs, tumor targeting can be described as the process by which mNPs reach the tumor mass (as a tissue), while tumor cell targeting refers to the specific interaction of mNPs with tumor cells once they have reached the tumor tissue. In this review, we conduct a critical analysis of key challenges that must be addressed for the successful targeting of either tumor tissue or cancer cells within the tumor tissue. Additionally, we explore essential features necessary for the smart design of theranostic mNPs, where 'smart design' refers to the process involving advanced consideration of the physicochemical features of the mNPs, targeting motifs, and physiological barriers that must be overcome for successful tumor targeting and/or tumor cell targeting.

Two photon imaging probe with highly efficient autofluorescence collection at high scattering and deep imaging conditions

In this paper, we present a 2-photon imaging probe system featuring a novel fluorescence collection method with improved and reliable efficiency. The system aims to miniaturize the potential of 2-photon imaging in the metabolic and morphological characterization of cervical tissue at sub-micron resolution over large imaging depths into a flexible and clinically viable platform towards the early detection of cancers. Clinical implementation of such a probe system is challenging due to inherently low levels of autofluorescence, particularly when imaging deep in highly scattering tissues. For an efficient collection of fluorescence signals, our probe employs 12 0.5 NA collection fibers arranged around a miniaturized excitation objective. By bending and terminating a multitude of collection fibers at a specific angle, we increase collection area and directivity significantly. Positioning of these fibers allows the collection of fluorescence photons scattered away from their ballistic trajectory multiple times, which offers a system collection efficiency of 4%, which is 55% of what our bench-top microscope with 0.75 NA objective achieves. We demonstrate that the collection efficiency is largely maintained even at high scattering conditions and high imaging depths. Radial symmetry of arrangement maintains uniformity of collection efficiency across the whole FOV. Additionally, our probe can image at different tissue depths via axial actuation by a dc servo motor, allowing depth dependent tissue characterization. We designed our probe to perform imaging at 775 nm, targeting 2-photon autofluorescence from NAD(P)H and FAD molecules, which are often used in metabolic tissue characterization. An air core photonic bandgap fiber delivers laser pulses of 100 fs duration to the sample. A miniaturized objective designed with commercially available lenses of 3 mm diameter focuses the laser beam on tissue, attaining lateral and axial imaging resolutions of 0.66 µm and 4.65 µm, respectively. Characterization results verify that our probe achieves collection efficiency comparable to our optimized bench-top 2-photon imaging microscope, minimally affected by imaging depth and radial positioning. We validate autofluorescence imaging capability with excised porcine vocal fold tissue samples. Images with 120 µm FOV and 0.33 µm pixel sizes collected at 2 fps confirm that the 300 µm imaging depth was achieved.

DNAzyme-based ultrasensitive immunoassay: Recent advances and emerging trends

Immunoassay, as the most commonly used method for protein detection, is simple to operate and highly specific. Sensitivity improvement is always the thrust of immunoassays, especially for the detection of trace quantities. The emergence of artificial enzyme, i.e., DNAzyme, provides a novel approach to improve the detection sensitivity of immunoassay. Simultaneously, its advantages of simple synthesis and high stability enable low cost, broad applicability and long shelf life for immunoassay. In this review, we summarized the recent advances in DNAzyme-based immunoassay. First, we summarized the existing different DNAzymes based on their catalytic activities. Next, the common signal amplification strategies used for DNAzyme-based immunoassays were reviewed to cater to diverse detection requirements. Following, the wide applications in disease diagnosis, environmental monitoring and food safety were discussed. Finally, the current challenges and perspectives on the future development of DNAzyme-based immunoassays were also provided.

Activity-Based Fluorescence Diagnostics for Cancer

Fluorescence imaging is one of the most promising approaches to achieve intraoperative assessment of the tumor/normal tissue margins during cancer surgery. This is critical to improve the patients' prognosis, and therefore various molecular fluorescence imaging probes have been developed for the identification of cancer lesions during surgery. Among them, "activatable" fluorescence probes that react with cancer-specific biomarker enzymes to generate fluorescence signals have great potential for high-contrast cancer imaging due to their low background fluorescence and high signal amplification by enzymatic turnover. Over the past two decades, activatable fluorescence probes employing various fluorescence control mechanisms have been developed worldwide for this purpose. Furthermore, new biomarker enzymatic activities for specific types of cancers have been identified, enabling visualization of various types of cancers with high sensitivity and specificity. This Review focuses on recent advances in the design, function and characteristics of activatable fluorescence probes that target cancer-specific enzymatic activities for cancer imaging and also discusses future prospects in the field of activity-based diagnostics for cancer.

Tissue clearing method in visualization of cancer progression and metastasis

Since various imaging modalities have been developed, cancer metastasis can be detected from an early stage. However, limitations still exist, especially in terms of spatial resolution. Tissue-clearing technology has emerged as a new imaging modality in cancer research, which has been developed and utilized for a long time mainly in neuroscience field. This method enables us to detect cancer metastatic foci with single-cell resolution at whole mouse body/organ level. On top of that, 3D images of cancer metastasis of whole mouse organs make it easy to understand their characteristics. Recently, further applications of tissue clearing methods were reported in combination with reporter systems, labeling, and machine learning. In this review, we would like to provide an overview of this technique and current applications in cancer research and discuss their potentials and limitations.

Developing a novel image marker to predict the clinical outcome of neoadjuvant chemotherapy (NACT) for ovarian cancer patients

OBJECTIVE

Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the clinical outcomes to NACT vary significantly among different subgroups. Partial responses to NACT may lead to suboptimal debulking surgery, which will result in adverse prognosis. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy prognosis prediction of NACT at an early stage.

METHODS

For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. This cluster was used as input for developing and optimizing support vector machine (SVM) based classifiers, which indicated the likelihood of receiving suboptimal cytoreduction after the NACT treatment. Two different kernels for SVM algorithm were explored and compared. A total of 42 ovarian cancer cases were retrospectively collected to validate the scheme. A nested leave-one-out cross-validation framework was adopted for model performance assessment.

RESULTS

The results demonstrated that the model with a Gaussian radial basis function kernel SVM yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.806 ± 0.078. Meanwhile, this model achieved overall accuracy (ACC) of 83.3%, positive predictive value (PPV) of 81.8%, and negative predictive value (NPV) of 83.9%.

CONCLUSION

This study provides meaningful information for the development of radiomics based image markers in NACT treatment outcome prediction.

A bubble-enhanced lanthanide-doped up/down-conversion platform with tumor microenvironment response for dual-modal photoacoustic and near-infrared-II fluorescence imaging

As an important tumor diagnosis strategy in precision medicine, multimodal imaging has been widely studied. However, the weak imaging signal with low spatial resolution and the constant signal of lack of specific activation severely limit its disease diagnosis. Herein, a bubble-enhanced lanthanide-based up/down-conversion platform with tumor microenvironment response for dual-mode imaging, LDNP@DMSN-Au@CaCO3 nanoparticles (named as LDAC NPs) were successfully developed. Combining the advantages of photoacoustic imaging (PAI) and the second near-infrared window (NIR-II) fluorescence imaging (FI), significantly improved the accuracy of diseases diagnosis. LDAC NPs with flower-like structure were synthesized through the encapsulation of uniform lanthanide-doped nanoparticles (NaYbF4:Ce,Er@NaYF4 named LDNPs) with dendritic mesoporous silica (DMSN). The gold nanoparticles (Au NPs) were then in situ grown on the surface of DMSN and the surface were finally coated with a layer of calcium carbonate (CaCO3). Under the excitation of the 980 nm laser, LDNPs showed strong emission of NIR-II at 1550 nm due to the doping of Ce and Er ions, showcasing excellent spatial resolution and deep tissue penetration characteristics, while the resulting visible light emission (540 nm) enables Au NPs to generate PAI signals with the aid of LDNPs via the fluorescence resonance energy transfer effect. In acidic tumoral environment, CaCO3 layer could produce CO2 microbubbles, and the PAI signals of LDAC NPs could be further enhanced with the generation of CO2 bubbles due to the bubble cavitation effect. Simultaneously, the NIR-II FI of LDAC NPs was self-enhanced with the degradation of the CaCO3. This intelligent nanoparticle with stimulus-activated dual-mode imaging capability holds great promise in future precision diagnostics.

Iterative nanoparticle bioengineering enabled by x-ray fluorescence imaging

Nanoparticles (NPs) are currently developed for drug delivery and molecular imaging. However, they often get intercepted before reaching their target, leading to low targeting efficacy and signal-to-noise ratio. They tend to accumulate in organs like lungs, liver, kidneys, and spleen. The remedy is to iteratively engineer NP surface properties and administration strategies, presently a time-consuming process that includes organ dissection at different time points. To improve this, we propose a rapid iterative approach using whole-animal x-ray fluorescence (XRF) imaging to systematically evaluate NP distribution in vivo. We applied this method to molybdenum-based NPs and clodronate liposomes for tumor targeting with transient macrophage depletion, leading to reduced accumulations in lungs and liver and eventual tumor detection. XRF computed tomography (XFCT) provided 3D insight into NP distribution within the tumor. We validated the results using a multiscale imaging approach with dye-doped NPs and gene expression analysis for nanotoxicological profiling. XRF imaging holds potential for advancing therapeutics and diagnostics in preclinical pharmacokinetic studies.

A systematic review on deep learning-based automated cancer diagnosis models

Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated diagnosis of cancer patients. Initially, various DL models for cancer diagnosis are presented. Five major categories of cancers such as breast, lung, liver, brain and cervical cancer are considered. As these categories of cancers have a very high percentage of occurrences with high mortality rate. The comparative analysis of different types of DL models is drawn for the diagnosis of cancer at early stages by considering the latest research articles from 2016 to 2022. After comprehensive comparative analysis, it is found that most of the researchers achieved appreciable accuracy with implementation of the convolutional neural network model. These utilized the pretrained models for automated diagnosis of cancer patients. Various shortcomings with the existing DL-based automated cancer diagnosis models are also been presented. Finally, future directions are discussed to facilitate further research for automated diagnosis of cancer patients.

Protein-guided biomimetic nanomaterials: a versatile theranostic nanoplatform for biomedical applications

Over the years, bioinspired mineralization-based approaches have been applied to synthesize multifunctional organic-inorganic nanocomposites. These nanocomposites can address the growing demands of modern biomedical applications. Proteins, serving as vital biological templates, play a pivotal role in the nucleation and growth processes of various organic-inorganic nanocomposites. Protein-mineralized nanomaterials (PMNMs) have attracted significant interest from researchers due to their facile and convenient preparation, strong physiological activity, stability, impressive biocompatibility, and biodegradability. Nevertheless, few comprehensive reviews have expounded on the progress of these nanomaterials in biomedicine. This article systematically reviews the principles and strategies for constructing nanomaterials using protein-directed biomineralization and biomimetic mineralization techniques. Subsequently, we focus on their recent applications in the biomedical field, encompassing areas such as bioimaging, as well as anti-tumor, anti-bacterial, and anti-inflammatory therapies. Furthermore, we discuss the challenges encountered in practical applications of these materials and explore their potential in future applications. This review aspired to catalyze the continued development of these bioinspired nanomaterials in drug development and clinical diagnosis, ultimately contributing to the fields of precision medicine and translational medicine.

Gut microbiota and its therapeutic implications in tumor microenvironment interactions

The development of cancer is not just the growth and proliferation of a single transformed cell, but its tumor microenvironment (TME) also coevolves with it, which is primarily involved in tumor initiation, development, metastasis, and therapeutic responses. Recent years, TME has been emerged as a potential target for cancer diagnosis and treatment. However, the clinical efficacy of treatments targeting the TME, especially its specific components, remains insufficient. In parallel, the gut microbiome is an essential TME component that is crucial in cancer immunotherapy. Thus, assessing and constructing frameworks between the gut microbiota and the TME can significantly enhance the exploration of effective treatment strategies for various tumors. In this review the role of the gut microbiota in human cancers, including its function and relationship with various tumors was summarized. In addition, the interaction between the gut microbiota and the TME as well as its potential applications in cancer therapeutics was described. Furthermore, it was summarized that fecal microbiota transplantation, dietary adjustments, and synthetic biology to introduce gut microbiota-based medical technologies for cancer treatment. This review provides a comprehensive summary for uncovering the mechanism underlying the effects of the gut microbiota on the TME and lays a foundation for the development of personalized medicine in further studies.

Synthesis and Bioapplication of Emerging Nanomaterials of Hafnium

A significant amount of progress in nanotechnology has been made due to the development of engineered nanoparticles. The use of metallic nanoparticles for various biomedical applications has been extensively investigated. Biomedical research is highly focused on them because of their inert nature, nanoscale structure, and similar size to many biological molecules. The intrinsic characteristics of these particles, including electronic, optical, physicochemical, and surface plasmon resonance, that can be altered by altering their size, shape, environment, aspect ratio, ease of synthesis, and functionalization properties, have led to numerous biomedical applications. Targeted drug delivery, sensing, photothermal and photodynamic therapy, and imaging are some of these. The promising clinical results of NBTXR3, a high-Z radiosensitizing nanomaterial derived from hafnium, have demonstrated translational potential of this metal. This radiosensitization approach leverages the dependence of energy attenuation on atomic number to enhance energy-matter interactions conducive to radiation therapy. High-Z nanoparticle localization in tumor issue differentially increases the effect of ionizing radiation on cancer cells versus nearby healthy ones and mitigates adverse effects by reducing the overall radiation burden. This principle enables material multifunctionality as contrast agents in X-ray-based imaging. The physiochemical properties of hafnium (Z = 72) are particularly advantageous for these applications. A well-placed K-edge absorption energy and high mass attenuation coefficient compared to elements in human tissue across clinical energy ranges leads to significant attenuation. Chemical reactivity allows for variety in nanoparticle synthesis, composition, and functionalization. Nanoparticles such as hafnium oxide exhibit excellent biocompatibility due to physiochemical inertness prior to incidence with ionizing radiation. Additionally, the optical and electronic properties are applicable in biosensing, optical component coatings, and semiconductors. The wide interest has prompted extensive research in design and synthesis to facilitate property fine-tuning. This review summarizes synthetic methods for hafnium-based nanomaterials and applications in therapy, imaging, and biosensing with a mechanistic focus. A discussion and future perspective section highlights clinical progress and elaborates on current challenges. By focusing on factors impacting applicational effectiveness and examining limitations this review aims to support researchers and expedite clinical translation of future hafnium-based nanomedicine.

Registry-derived stage (RD-Stage) for capturing cancer stage at diagnosis for endometrial cancer

BACKGROUND

Capture of cancer stage at diagnosis is important yet poorly reported by health services to population-based cancer registries. In this paper we describe current completeness of stage information for endometrial cancer available in Australian cancer registries; and develop and validate a set of rules to enable cancer registry medical coders to calculate stage using data available to them (registry-derived stage or 'RD-Stage').

METHODOLOGY

Rules for deriving RD-stage (Endometrial carcinoma) were developed using the American Joint Commission on Cancer (AJCC) TNM (tumour, nodes, metastasis) Staging System (8th Edition). An expert working group comprising cancer specialists responsible for delivering cancer care, epidemiologists and medical coders reviewed and endorsed the rules. Baseline completeness of data fields required to calculate RD-Stage, and calculation of the proportion of cases for whom an RD stage could be assigned, was assessed across each Australian jurisdiction. RD-Stage (Endometrial cancer) was calculated by Victorian Cancer Registry (VCR) medical coders and compared with clinical stage recorded by the patient's treating clinician and captured in the National Gynae-Oncology Registry (NGOR).

RESULTS

The necessary data completeness level for calculating RD-Stage (Endometrial carcinoma) across various Australian jurisdictions varied from 0 to 89%. Three jurisdictions captured degree of spread of cancer, rendering RD-Stage unable to be calculated. RD-Stage (Endometrial carcinoma) could not be derived for 64/485 (13%) cases and was not captured for 44/485 (9%) cases in NGOR. At stage category level (I, II, III, IV), there was concordance between RD-Stage and NGOR captured stage in 393/410 (96%) of cases (95.8%, Kendall's coefficient = 0.95).

CONCLUSION

A lack of consistency in data captured by, and data sources reporting to, population-based cancer registries meant that it was not possible to provide national endometrial carcinoma stage data at diagnosis. In a sample of Victorian cases, where surgical pathology was available, there was very good concordance between RD-Stage (Endometrial carcinoma) and clinician-recorded stage data available from NGOR. RD-Stage offers promise in capturing endometrial cancer stage at diagnosis for population epidemiological purposes when it is not provided by health services, but requires more extensive validation.

In vivo imaging of prostate tumor-targeted folic acid conjugated quantum dots

Cancer is a major threat to human health; thus, early detection is imperative for successful management. Rapid diagnosis can be achieved by imaging primary (subcutaneous) tumors using fluorophores conjugated with tumor markers. Here, the application of biocompatible, quantum efficient, monodisperse, and photostable polymer-coated quantum dots (PQDs) is demonstrated for targeted prostate tumor imaging in living SCID mice. Briefly, PQDs (blue) are conjugated to folic acid (FA-PQDs) using DCC-NHS chemistry. Initially, in vitro targeted imaging via FA-PQDs is evaluated in LNCaP cells. The confocal microscopic evaluation demonstrates the uptake of FA-PQDs. To understand the dispersion of PQDs in vivo, the biodistribution of PQDs is assessed at different time intervals (1- 180 min) using whole-body fluorescence imaging and computed tomography (CT) scan. PQDs are seen to accumulate in organs like the liver, kidneys, spleen, lungs, and urinary bladder within 60 min, however, PQDs are not observed at 180 min indicating renal clearance. Further, to target the prostate tumor (~ 200 mm3) in mice, FA-PQDs are injected intravenously, and whole-body fluorescence imaging along with a CT scan is recorded. FA-PQDs are seen at the tumor site as compared to PQDs. The results confirm that the FA-PQDs function as excellent nanoprobes for targeted tumor imaging in vivo.

Cancer care in Needle Hospital, Hargeisa, Somaliland

Somaliland is an autonomous region in the northern part of Somalia that declared its independence in 1991. It is a low-income country (LIC) with a population size of 5.7 million with a gross domestic product per capita of $775. Health services are delivered by public, private and non-governmental organisations. The public health care system in Somaliland is facing huge challenges. Seven percent of the population suffers from non-communicable diseases, but data on cancer incidence and mortality are not available. Much of the emphasis in public health has been placed on primary care and maternal and child health. There is still a large gap in cancer prevention, early detection and screening in the country. Additionally, there is no cancer registry or published data on cancer. Currently, there are a few private hospitals that provide chemotherapy services in Somaliland of which Needle Hospital is one. Services provided in this hospital include medical oncology for all solid tumours, palliative care, follow-up and cancer health education. The hospital provides services for patients from Somaliland and neighbouring countries including Djibouti, Somalia and Ethiopia. As a new oncology clinic in an LIC, the clinic is facing many challenges, like the absence of a multidisciplinary tumour board, presentation of patients at the advanced stage of tumours and poor cancer awareness in the general population.

Near-Infrared Rechargeable Persistent Luminescence Nanoparticles for Biomedical Implants In Vivo Noninvasive Bioimaging

Luminescent imaging has garnered significant attention for in vivo tracking of biomedical implants during and after surgery due to its human friendliness, affordability, and high sensitivity. However, conventional fluorescent probes are susceptible to background autofluorescence interference from living tissues, often resulting in poor signal-to-noise ratios. Herein, we report a background interference-free persistent luminescent implant (PLI) with excellent persistent luminescence (PL) performance, which can be clearly and long-term detected by an optical imaging system after implantation. Rechargeable near-infrared persistent luminescence nanoparticles (PLNPs) were prepared first via a simple hydrothermal approach and then modified by PEGylation to improve their hydrophilicity, biocompatibility, and compatibility with polymer substrates. The PEGylated PLNPs were facilely complexed into a polymer matrix to fabricate the PLI. The obtained PLIs can well inherit the PL properties of PLNPs, exhibiting good PL optical imaging performance without tissue autofluorescence interference. Furthermore, both PLNPs and PLIs possess good biocompatibility, and the addition of PLNPs has no negative impact on the biocompatibility of the polymer matrix. This work fully utilizes the luminescent properties of PLNPs and adapts this PL to the field of biomedical implant imaging, which provides new insight for designing biomedical imaging systems.

Oncological Horizons: The Synergy of Medical and Surgical Innovations in Cancer Treatment

The landscape of cancer treatment has witnessed a remarkable transformation in recent years, marked by the convergence of medical and surgical innovations. Historically, cancer therapy faced challenges, including limited efficacy and severe side effects. This narrative review explores the historical progression of cancer treatments, shedding light on significant breakthroughs in both medical and surgical oncology. It comprehensively addresses the medical domain, covering chemotherapy, targeted therapies, immunotherapy, hormonal treatments, and radiological procedures. Simultaneously, it delves into the surgical realm, discussing the evolution of surgical techniques, minimally invasive procedures, and the role of surgery across various stages of cancer. The article emphasizes the fusion of medical and surgical approaches, highlighting neoadjuvant and adjuvant therapies and the significance of multidisciplinary tumor boards. It also addresses innovations, challenges, and the pivotal role of patient-centered care. Furthermore, it offers insights into the future directions and forecasts in the constantly evolving field of integrated oncological care. This review provides a comprehensive understanding of the dynamic and transformative nature of cancer treatment, reflecting the unwavering commitment of the medical and surgical communities in the ongoing fight against cancer.

Free automatic software for quality assurance of computed tomography calibration, edges and radiomics metrics reproducibility

PURPOSE

To develop a QA procedure, easy to use, reproducible and based on open-source code, to automatically evaluate the stability of different metrics extracted from CT images: Hounsfield Unit (HU) calibration, edge characterization metrics (contrast and drop range) and radiomic features.

METHODS

The QA protocol was based on electron density phantom imaging. Home-made open-source Python code was developed for the automatic computation of the metrics and their reproducibility analysis. The impact on reproducibility was evaluated for different radiation therapy protocols, and phantom positions within the field of view and systems, in terms of variability (Shapiro-Wilk test for 15 repeated measurements carried out over three days) and comparability (Bland-Altman analysis and Wilcoxon Rank Sum Test or Kendall Rank Correlation Coefficient).

RESULTS

Regarding intrinsic variability, most metrics followed a normal distribution (88% of HU, 63% of edge parameters and 82% of radiomic features). Regarding comparability, HU and contrast were comparable in all conditions, and drop range only in the same CT scanner and phantom position. The percentages of comparable radiomic features independent of protocol, position and system were 59%, 78% and 54%, respectively. The non-significantly differences in HU calibration curves obtained for two different institutions (7%) translated in comparable Gamma Index G (1 mm, 1%, >99%).

CONCLUSIONS

An automated software to assess the reproducibility of different CT metrics was successfully created and validated. A QA routine proposal is suggested.

Dynamic ultrasound molecular-targeted imaging of senescence in evaluation of lapatinib resistance in HER2-positive breast cancer

BACKGROUND

Prolonged treatment of HER2+ breast cancer with lapatinib (LAP) causes cellular senescence and acquired drug resistance, which often associating with poor prognosis for patients. We aim to explore the correlation between cellular senescence and LAP resistance in HER2+ breast cancer, screen for molecular marker of reversible senescence, and construct targeted nanobubbles for ultrasound molecular imaging to dynamically evaluate LAP resistance.

METHODS AND RESULTS

In this study, we established a new cellular model of reversible cellular senescence using LAP and HER2+ breast cancer cells and found that reversible senescence contributed to LAP resistance in HER2+ breast cancer. Then, we identified ecto-5'-nucleotidase (NT5E) as a marker of reversible senescence in HER2+ breast cancer. Based on this, we constructed NT5E-targeted nanobubbles (NT5E-FITC-NBs) as a new molecular imaging modality which could both target reversible senescent cells and be used for ultrasound imaging. NT5E-FITC-NBs showed excellent physical and imaging characteristics. As an ultrasound contrast agent, NT5E-FITC-NBs could accurately identify reversible senescent cells both in vitro and in vivo.

CONCLUSIONS

Our data demonstrate that cellular senescence-based ultrasound-targeted imaging can identify reversible senescence and evaluate LAP resistance effectively in HER2+ breast cancer cells, which has the potential to improve cancer treatment outcomes by altering therapeutic strategies ahead of aggressive recurrences.

A Circular Box-Based Deep Learning Model for the Identification of Signet Ring Cells from Histopathological Images

Signet ring cell (SRC) carcinoma is a particularly serious type of cancer that is a leading cause of death all over the world. SRC carcinoma has a more deceptive onset than other carcinomas and is mostly encountered in its later stages. Thus, the recognition of SRCs at their initial stages is a challenge because of different variants and sizes and illumination changes. The recognition process of SRCs at their early stages is costly because of the requirement for medical experts. A timely diagnosis is important because the level of the disease determines the severity, cure, and survival rate of victims. To tackle the current challenges, a deep learning (DL)-based methodology is proposed in this paper, i.e., custom CircleNet with ResNet-34 for SRC recognition and classification. We chose this method because of the circular shapes of SRCs and achieved better performance due to the CircleNet method. We utilized a challenging dataset for experimentation and performed augmentation to increase the dataset samples. The experiments were conducted using 35,000 images and attained 96.40% accuracy. We performed a comparative analysis and confirmed that our method outperforms the other methods.

Advances in dendritic cell vaccination therapy of cancer

Traditionally, vaccines have helped eradication of several infectious diseases and also saved millions of lives in the human history. Those prophylactic vaccines have acted through inducing immune responses against a live attenuated, killed organism or antigenic subunits to protect the recipient against a real infection caused by the pathogenic microorganism. Nevertheless, development of anticancer vaccines as valuable targets in human health has faced challenges and requires further optimizations. Dendritic cells (DCs) are the most potent antigen presenting cells (APCs) that play essential roles in tumor immunotherapies through induction of CD8+ T cell immunity. Accordingly, various strategies have been tested to employ DCs as therapeutic vaccines for exploiting their activity against tumor cells. Application of whole tumor cells or purified/recombinant antigen peptides are the most common approaches for pulsing DCs, which then are injected back into the patients. Although some hopeful results are reported for a number of DC vaccines tested in animal and clinical trials of cancer patients, such approaches are still inefficient and require optimization. Failure of DC vaccination is postulated due to immunosuppressive tumor microenvironment (TME), overexpression of checkpoint proteins, suboptimal avidity of tumor-associated antigen (TAA)-specific T lymphocytes, and lack of appropriate adjuvants. In this review, we have an overview of the current experiments and trials evaluated the anticancer efficacy of DC vaccination as well as focusing on strategies to improve their potential including combination therapy with immune checkpoint inhibitors (ICIs).

Recent Advances of Metal-Polyphenol Coordination Polymers for Biomedical Applications

Nanomedicine has provided cutting-edge technologies and innovative methods for modern biomedical research, offering unprecedented opportunities to tackle crucial biomedical issues. Nanomaterials with unique structures and properties can integrate multiple functions to achieve more precise diagnosis and treatment, making up for the shortcomings of traditional treatment methods. Among them, metal-polyphenol coordination polymers (MPCPs), composed of metal ions and phenolic ligands, are considered as ideal nanoplatforms for disease diagnosis and treatment. Recently, MPCPs have been extensively investigated in the field of biomedicine due to their facile synthesis, adjustable structures, and excellent biocompatibility, as well as pH-responsiveness. In this review, the classification of various MPCPs and their fabrication strategies are firstly summarized. Then, their significant achievements in the biomedical field such as biosensing, drug delivery, bioimaging, tumor therapy, and antibacterial applications are highlighted. Finally, the main limitations and outlooks regarding MPCPs are discussed.

Digital Transformation of Cancer Care in the Era of Big Data, Artificial Intelligence and Data-Driven Interventions: Navigating the Field

OBJECTIVES

To navigate the field of digital cancer care and define and discuss key aspects and applications of big data analytics, artificial intelligence (AI), and data-driven interventions.

DATA SOURCES

Peer-reviewed scientific publications and expert opinion.

CONCLUSION

The digital transformation of cancer care, enabled by big data analytics, AI, and data-driven interventions, presents a significant opportunity to revolutionize the field. An increased understanding of the lifecycle and ethics of data-driven interventions will enhance development of innovative and applicable products to advance digital cancer care services.

IMPLICATIONS FOR NURSING PRACTICE

As digital technologies become integrated into cancer care, nurse practitioners and scientists will be required to increase their knowledge and skills to effectively use these tools to the patient's benefit. An enhanced understanding of the core concepts of AI and big data, confident use of digital health platforms, and ability to interpret the outputs of data-driven interventions are key competencies. Nurses in oncology will play a crucial role in patient education around big data and AI, with a focus on addressing any arising questions, concerns, or misconceptions to foster trust in these technologies. Successful integration of data-driven innovations into oncology nursing practice will empower practitioners to deliver more personalized, effective, and evidence-based care.

New opportunities for RGD-engineered metal nanoparticles in cancer

The advent of nanotechnology has opened new possibilities for bioimaging. Metal nanoparticles (such as gold, silver, iron, copper, etc.) hold tremendous potential and offer enormous opportunities for imaging and diagnostics due to their broad optical characteristics, ease of manufacturing technique, and simple surface modification. The arginine-glycine-aspartate (RGD) peptide is a three-amino acid sequence that seems to have a considerably greater ability to adhere to integrin adhesion molecules that exclusively express on tumour cells. RGD peptides act as the efficient tailoring ligand with a variety of benefits including non-toxicity, greater precision, rapid clearance, etc. This review focuses on the possibility of non-invasive cancer imaging using metal nanoparticles with RGD assistance.

Dendrimers: Advancements and Potential Applications in Cancer Diagnosis and Treatment-An Overview

Cancer is a leading cause of death worldwide, and the main treatment methods for this condition are surgery, chemotherapy, and radiotherapy. These treatment methods are invasive and can cause severe adverse reactions among organisms, so nanomaterials are increasingly used as structures for anticancer therapies. Dendrimers are a type of nanomaterial with unique properties, and their production can be controlled to obtain compounds with the desired characteristics. These polymeric molecules are used in cancer diagnosis and treatment through the targeted distribution of some pharmacological substances. Dendrimers have the ability to fulfill several objectives in anticancer therapy simultaneously, such as targeting tumor cells so that healthy tissue is not affected, controlling the release of anticancer agents in the tumor microenvironment, and combining anticancer strategies based on the administration of anticancer molecules to potentiate their effect through photothermal therapy or photodynamic therapy. The purpose of this review is to summarize and highlight the possible uses of dendrimers regarding the diagnosis and treatment of oncological conditions.

Application of molecular dynamics simulation in self-assembled cancer nanomedicine

Self-assembled nanomedicine holds great potential in cancer theragnostic. The structures and dynamics of nanomedicine can be affected by a variety of non-covalent interactions, so it is essential to ensure the self-assembly process at atomic level. Molecular dynamics (MD) simulation is a key technology to link microcosm and macroscale. Along with the rapid development of computational power and simulation methods, scientists could simulate the specific process of intermolecular interactions. Thus, some experimental observations could be explained at microscopic level and the nanomedicine synthesis process would have traces to follow. This review not only outlines the concept, basic principle, and the parameter setting of MD simulation, but also highlights the recent progress in MD simulation for self-assembled cancer nanomedicine. In addition, the physicochemical parameters of self-assembly structure and interaction between various assembled molecules under MD simulation are also discussed. Therefore, this review will help advanced and novice researchers to quickly zoom in on fundamental information and gather some thought-provoking ideas to advance this subfield of self-assembled cancer nanomedicine.

Fat quantification: Imaging methods and clinical applications in cancer

Recently, the study of the relationship between lipid metabolism and cancer has evolved. The characteristics of intratumoral and peritumoral fat are distinct and changeable during cancer development. Subcutaneous and visceral adipose tissue are also associated with cancer prognosis. In non-invasive imaging, fat quantification parameters such as controlled attenuation parameter, fat volume fraction, and proton density fat fraction from different imaging methods complement conventional images by providing concrete fat information. Therefore, measuring the changes of fat content for further understanding of cancer characteristics has been applied in both research and clinical settings. In this review, the authors summarize imaging advances in fat quantification and highlight their clinical applications in cancer precaution, auxiliary diagnosis and classification, therapy response monitoring, and prognosis.

Multimodal data fusion for cancer biomarker discovery with deep learning

Technological advances now make it possible to study a patient from multiple angles with high-dimensional, high-throughput multi-scale biomedical data. In oncology, massive amounts of data are being generated ranging from molecular, histopathology, radiology to clinical records. The introduction of deep learning has significantly advanced the analysis of biomedical data. However, most approaches focus on single data modalities leading to slow progress in methods to integrate complementary data types. Development of effective multimodal fusion approaches is becoming increasingly important as a single modality might not be consistent and sufficient to capture the heterogeneity of complex diseases to tailor medical care and improve personalised medicine. Many initiatives now focus on integrating these disparate modalities to unravel the biological processes involved in multifactorial diseases such as cancer. However, many obstacles remain, including lack of usable data as well as methods for clinical validation and interpretation. Here, we cover these current challenges and reflect on opportunities through deep learning to tackle data sparsity and scarcity, multimodal interpretability, and standardisation of datasets.

Solid Lipid Nanoparticles: Multitasking Nano-Carriers for Cancer Treatment

Despite all the advances seen in recent years, the severe adverse effects and low specificity of conventional chemotherapy are still challenging problems regarding cancer treatment. Nanotechnology has helped to address these questions, making important contributions in the oncological field. The use of nanoparticles has allowed the improvement of the therapeutic index of several conventional drugs and facilitates the tumoral accumulation and intracellular delivery of complex biomolecules, such as genetic material. Among the wide range of nanotechnology-based drug delivery systems (nanoDDS), solid lipid nanoparticles (SLNs) have emerged as promising systems for delivering different types of cargo. Their solid lipid core, at room and body temperature, provides SLNs with higher stability than other formulations. Moreover, SLNs offer other important features, namely the possibility to perform active targeting, sustained and controlled release, and multifunctional therapy. Furthermore, with the possibility to use biocompatible and physiologic materials and easy scale-up and low-cost production methods, SLNs meet the principal requirements of an ideal nanoDDS. The present work aims to summarize the main aspects related to SLNs, including composition, production methods, and administration routes, as well as to show the most recent studies about the use of SLNs for cancer treatment.

Radiolabeled nanomaterial for cancer diagnostics and therapeutics: principles and concepts

In the last three decades, radiopharmaceuticals have proven their effectiveness for cancer diagnosis and therapy. In parallel, the advances in nanotechnology have fueled a plethora of applications in biology and medicine. A convergence of these disciplines has emerged more recently with the advent of nanotechnology-aided radiopharmaceuticals. Capitalizing on the unique physical and functional properties of nanoparticles, radiolabeled nanomaterials or nano-radiopharmaceuticals have the potential to enhance imaging and therapy of human diseases. This article provides an overview of various radionuclides used in diagnostic, therapeutic, and theranostic applications, radionuclide production through different techniques, conventional radionuclide delivery systems, and advancements in the delivery systems for nanomaterials. The review also provides insights into fundamental concepts necessary to improve currently available radionuclide agents and formulate new nano-radiopharmaceuticals.

Molecular MRI-Based Monitoring of Cancer Immunotherapy Treatment Response

Immunotherapy constitutes a paradigm shift in cancer treatment. Its FDA approval for several indications has yielded improved prognosis for cases where traditional therapy has shown limited efficiency. However, many patients still fail to benefit from this treatment modality, and the exact mechanisms responsible for tumor response are unknown. Noninvasive treatment monitoring is crucial for longitudinal tumor characterization and the early detection of non-responders. While various medical imaging techniques can provide a morphological picture of the lesion and its surrounding tissue, a molecular-oriented imaging approach holds the key to unraveling biological effects that occur much earlier in the immunotherapy timeline. Magnetic resonance imaging (MRI) is a highly versatile imaging modality, where the image contrast can be tailored to emphasize a particular biophysical property of interest using advanced engineering of the imaging pipeline. In this review, recent advances in molecular-MRI based cancer immunotherapy monitoring are described. Next, the presentation of the underlying physics, computational, and biological features are complemented by a critical analysis of the results obtained in preclinical and clinical studies. Finally, emerging artificial intelligence (AI)-based strategies to further distill, quantify, and interpret the image-based molecular MRI information are discussed in terms of perspectives for the future.

XFCT-MRI hybrid multimodal contrast agents for complementary imaging

Multimodal contrast agents in biomedical imaging enable the collection of more comprehensive diagnostic information. In the present work, we design hybrid ruthenium-decorated superparamagnetic iron oxide nanoparticles (NPs) as the contrast agents for both magnetic resonance imaging (MRI) and X-ray fluorescence computed tomography (XFCT). The NPs are synthesized via a one-pot polyol hot injection route, in diethylene glycol. In vivo preclinical studies demonstrate the possibility of correlative bioimaging with these contrast agents. The complementarity allows accurate localization, provided by the high contrast of the soft tissues in MRI combined with the elemental selectivity of XFCT, leading to NP detection with high specificity and resolution. We envision that this multimodal imaging could find future applications for early tumor diagnosis, improved long-term treatment monitoring, and enhanced radiotherapy planning.

Technological advancements in cancer diagnostics: Improvements and limitations

BACKGROUND

Cancer is characterized by the rampant proliferation, growth, and infiltration of malignantly transformed cancer cells past their normal boundaries into adjacent tissues. It is the leading cause of death worldwide, responsible for approximately 19.3 million new diagnoses and 10 million deaths globally in 2020. In the United States alone, the estimated number of new diagnoses and deaths is 1.9 million and 609 360, respectively. Implementation of currently existing cancer diagnostic techniques such as positron emission tomography (PET), X-ray computed tomography (CT), and magnetic resonance spectroscopy (MRS), and molecular diagnostic techniques, have enabled early detection rates and are instrumental not only for the therapeutic management of cancer patients, but also for early detection of the cancer itself. The effectiveness of these cancer screening programs are heavily dependent on the rate of accurate precursor lesion identification; an increased rate of identification allows for earlier onset treatment, thus decreasing the incidence of invasive cancer in the long-term, and improving the overall prognosis. Although these diagnostic techniques are advantageous due to lack of invasiveness and easier accessibility within the clinical setting, several limitations such as optimal target definition, high signal to background ratio and associated artifacts hinder the accurate diagnosis of specific types of deep-seated tumors, besides associated high cost. In this review we discuss various imaging, molecular, and low-cost diagnostic tools and related technological advancements, to provide a better understanding of cancer diagnostics, unraveling new opportunities for effective management of cancer, particularly in low- and middle-income countries (LMICs).

RECENT FINDINGS

Herein we discuss various technological advancements that are being utilized to construct an assortment of new diagnostic techniques that incorporate hardware, image reconstruction software, imaging devices, biomarkers, and even artificial intelligence algorithms, thereby providing a reliable diagnosis and analysis of the tumor. Also, we provide a brief account of alternative low cost-effective cancer therapy devices (CryoPop®, LumaGEM®, MarginProbe®) and picture archiving and communication systems (PACS), emphasizing the need for multi-disciplinary collaboration among radiologists, pathologists, and other involved specialties for improving cancer diagnostics.

CONCLUSION

Revolutionary technological advancements in cancer imaging and molecular biology techniques are indispensable for the accurate diagnosis and prognosis of cancer.

Radiogenomic association of deep MR imaging features with genomic profiles and clinical characteristics in breast cancer

BACKGROUND

It has been believed that traditional handcrafted radiomic features extracted from magnetic resonance imaging (MRI) of tumors are normally shallow and low-ordered. Recent advancement in deep learning technology shows that the high-order deep radiomic features extracted automatically from tumor images can capture tumor heterogeneity in a more efficient way. We hypothesize that MRI-based deep radiomic phenotypes have significant associations with molecular profiles of breast cancer tumors. We aim to identify deep radiomic features (DRFs) from MRI, evaluate their significance in predicting breast cancer (BC) clinical characteristics and explore their associations with multi-level genomic factors.

METHODS

A denoising autoencoder was built to retrospectively extract 4,096 DRFs from 110 BC patients' MRI. Visualization and clustering were applied to these DRFs. Linear Mixed Effect models were used to test their associations with multi-level genomic features (GFs) (risk genes, gene signatures, and biological pathway activities) extracted from the same patients' mRNA expression profile. A Least Absolute Shrinkage and Selection Operator model was used to identify the most predictive DRFs for each clinical characteristic (tumor size (T), lymph node metastasis (N), estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status).

RESULTS

Thirty-six conventional radiomic features (CRFs) for 87 of the 110 BC patients provided by a previous study were used for comparison. More than 1,000 DRFs were associated with the risk genes, gene signatures, and biological pathways activities (adjusted P-value < 0.05). DRFs produced better performance in predicting T, N, ER, PR, and HER2 status (AUC > 0.9) using DRFs. These DRFs showed significant powers of stratifying patients, linking to relevant biological and clinical characteristics. As a contrast, only eight risk genes were associated with CRFs. The RFs performed worse in predicting clinical characteristics than DRFs.

CONCLUSIONS

The deep learning-based auto MRI features perform better in predicting BC clinical characteristics, which are more significantly associated with GFs than traditional semi-auto MRI features. Our radiogenomic approach for identifying MRI-based imaging signatures may pave potential pathways for the discovery of genetic mechanisms regulating specific tumor phenotypes and may enable a more rapid innovation of novel imaging modalities, hence accelerating their translation to personalized medicine.

Prospective Epigenetic Actions of Organo-Sulfur Compounds against Cancer: Perspectives and Molecular Mechanisms

Major epigenetic alterations, such as chromatin modifications, DNA methylation, and miRNA regulation, have gained greater attention and play significant roles in oncogenesis, representing a new paradigm in our understanding of cancer susceptibility. These epigenetic changes, particularly aberrant promoter hypermethylation, abnormal histone acetylation, and miRNA dysregulation, represent a set of epigenetic patterns that contribute to inappropriate gene silencing at every stage of cancer progression. Notably, the cancer epigenome possesses various HDACs and DNMTs, which participate in the histone modifications and DNA methylation. As a result, there is an unmet need for developing the epigenetic inhibitors against HDACs and DNMTs for cancer therapy. To date, several epigenetically active synthetic inhibitors of DNA methyltransferases and histone deacetylases have been developed. However, a growing body of research reports that most of these synthetic inhibitors have significant side effects and a narrow window of specificity for cancer cells. Targeting tumor epigenetics with phytocompounds that have the capacity to modulate abnormal DNA methylation, histone acetylation, and miRNAs expression is one of the evolving strategies for cancer prevention. Encouragingly, there are many bioactive phytochemicals, including organo-sulfur compounds that have been shown to alter the expression of key tumor suppressor genes, oncogenes, and oncogenic miRNAs through modulation of DNA methylation and histones in cancer. In addition to vitamins and microelements, dietary phytochemicals such as sulforaphane, PEITC, BITC, DADS, and allicin are among a growing list of naturally occurring anticancer agents that have been studied as an alternative strategy for cancer treatment and prevention. Moreover, these bioactive organo-sulfur compounds, either alone or in combination with other standard cancer drugs or phytochemicals, showed promising results against many cancers. Here, we particularly summarize and focus on the impact of specific organo-sulfur compounds on DNA methylation and histone modifications through targeting the expression of different DNMTs and HDACs that are of particular interest in cancer therapy and prevention.

Analyzing bronchoalveolar fluid derived small extracellular vesicles using single-vesicle SERS for non-small cell lung cancer detection

An emerging body of research by biologists and clinicians has demonstrated the clinical application of small extracellular vesicles (sEVs, also commonly referred to as exosomes) as biomarkers for cancer detections. sEVs isolated from various body fluids such as blood, saliva, urine, and cerebrospinal fluid have been used for biomarker discoveries with highly encouraging outcomes. Among the biomarkers discovered are those responsible for multiple cancer types and immune responses. These biomarkers are recapitulated from the tumor microenvironments. Yet, despite numerous discussions of sEVs in scientific literature, sEV-based biomarkers have so far played only a minor role for cancer diagnostics in the clinical setting, notably less so than other techniques such as imaging and biopsy. In this paper, we report the results of a pilot study (n = 10 from each of the patient and the control group) using bronchoalveolar lavage fluid to determine the presence of sEVs related to non-small cell lung cancer in twenty clinical samples examined using surface enhanced Raman spectroscopy (SERS).