Fluorescence guidance is routinely used in surgery to enhance perfusion contrast in multiple types of diseases. Pressure-enhanced sensing of tissue oxygenation (PRESTO) via fluorescence is a technique extensively analyzed here, that uses an FDA-approved human precursor molecule, 5-aminolevulinic acid (ALA), to stimulate a unique delayed fluorescence signal that is representative of tissue hypoxia. The ALA precontrast agent is metabolized in most tissues into a red fluorescent molecule, protoporphyrin IX (PpIX), which has both prompt fluorescence, indicative of the concentration, and a delayed fluorescence, that is amplified in low tissue oxygen situations. Applied pressure from palpation induces transient capillary stasis and a resulting transient PRESTO contrast, dominant when there is near hypoxia. This study examined the kinetics and behavior of this effect in both normal and tumor tissues, with a prolonged high PRESTO contrast (contrast to background of 7.3) across 5 tumor models, due to sluggish capillaries and inhibited vasodynamics. This tissue function imaging approach is a fundamentally unique tool for real-time palpation-induced tissue response in vivo, relevant for chronic hypoxia, such as vascular diseases or oncologic surgery.

A comprehensive understanding of a tumor is required for accurate diagnosis and effective treatment. However, currently, there is no single imaging modality that can provide sufficient information. Photoacoustic (PA) imaging is a hybrid imaging technique with high spatial resolution and detection sensitivity, which can be combined with ultrasound (US) imaging to provide both optical and acoustic contrast. Elastography can noninvasively map the elasticity distribution of biological tissue, which reflects pathological conditions. In this study, we incorporated PA elastography into a commercial US/PA imaging system to develop a tri-modality imaging system, which has been tested for tumor detection using four mice with different physiological conditions. The results show that this tri-modality imaging system can provide complementary information on acoustic, optical, and mechanical properties. The enabled visualization and dimension estimation of tumors can lead to a more comprehensive tissue characterization for diagnosis and treatment.

Detection of circulating tumor DNA (ctDNA) mutations, which are molecular biomarkers present in bodily fluids of cancer patients, can be applied for tumor diagnosis and prognosis monitoring. However, current profiling of ctDNA mutations relies primarily on polymerase chain reaction (PCR) and DNA sequencing and these techniques require preanalytical processing of blood samples, which are time-consuming, expensive, and tedious procedures that increase the risk of sample contamination. To overcome these limitations, here the engineering of a DNA/γPNA (gamma peptide nucleic acid) hybrid nanoreporter is disclosed for ctDNA biosensing via in situ profiling and recording of tumor-specific DNA mutations. The low tolerance of γPNA to single mismatch in base pairing with DNA allows highly selective recognition and recording of ctDNA mutations in peripheral blood. Owing to their remarkable biostability, the detached γPNA strands triggered by mutant ctDNA will be enriched in kidneys and cleared into urine for urinalysis. It is demonstrated that the nanoreporter has high specificity for ctDNA mutation in peripheral blood, and urinalysis of cleared γPNA can provide valuable information for tumor progression and prognosis evaluation. This work demonstrates the potential of the nanoreporter for urinary monitoring of tumor and patient prognosis through in situ biosensing of ctDNA mutations.

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 advancement of artificial intelligence (AI) for organ segmentation and tumor detection is propelled by the growing availability of computed tomography (CT) datasets with detailed, per-voxel annotations. However, these AI models often struggle with flexibility for partially annotated datasets and extensibility for new classes due to limitations in the one-hot encoding, architectural design, and learning scheme. To overcome these limitations, we propose a universal, extensible framework enabling a single model, termed Universal Model, to deal with multiple public datasets and adapt to new classes (e.g., organs/tumors). Firstly, we introduce a novel language-driven parameter generator that leverages language embeddings from large language models, enriching semantic encoding compared with one-hot encoding. Secondly, the conventional output layers are replaced with lightweight, class-specific heads, allowing Universal Model to simultaneously segment 25 organs and six types of tumors and ease the addition of new classes. We train our Universal Model on 3410 CT volumes assembled from 14 publicly available datasets and then test it on 6173 CT volumes from four external datasets. Universal Model achieves first place on six CT tasks in the Medical Segmentation Decathlon (MSD) public leaderboard and leading performance on the Beyond The Cranial Vault (BTCV) dataset. In summary, Universal Model exhibits remarkable computational efficiency (6× faster than other dataset-specific models), demonstrates strong generalization across different hospitals, transfers well to numerous downstream tasks, and more importantly, facilitates the extensibility to new classes while alleviating the catastrophic forgetting of previously learned classes. Codes, models, and datasets are available at https://github.com/ljwztc/CLIP-Driven-Universal-Model.

BACKGROUND: Despite advances in treatment, residual or recurrent tumors after definitive (chemo) radiotherapy for laryngeal and hypopharyngeal squamous cell carcinoma (SCC) remain a challenge in clinical management and require accurate and timely detection for optimal salvage therapy. This study aimed to compare the diagnostic value of Fluorine 18 (18F) fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) and diffusion-weighted magnetic resonance imaging (DW-MRI) in detecting residual or recurrent tumors after definitive (chemo) radiotherapy for laryngeal and hypopharyngeal SCC.
METHODS: A prospective study was conducted on 30 patients who presented with new symptoms after definitive (chemo) radiotherapy for laryngeal (n = 21) and hypopharyngeal (n = 9) carcinoma. Both 18F-FDG PET/CT and DW-MRI were performed and histopathologic analysis served as the standard of reference.
RESULTS: Histopathology showed 20 patients as positive and 10 as negative for tumors. 18F-FDG PET/CT detected all tumors correctly but was falsely positive in one case. DW-MRI detected tumors in 18 out of 20 positive patients and correctly excluded tumors in all negative patients. The sensitivity and specificity of 18F-FDG PET/CT were 100% and 90%, respectively, while the values for DW-MRI were 90% and 100%, respectively.
CONCLUSIONS: The study concludes that 18F-FDG PET/CT is slightly superior to DW-MRI in detecting residual or recurrent tumors after definitive (chemo) radiotherapy for laryngeal and hypopharyngeal SCC. The combined use of 18F-FDG PET/CT and DW-MRI can potentially improve specificity in therapy response evaluation.

There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive. A variety of imaging methods are available to detect brain tumors, including magnetic resonance imaging (MRI) and computed tomography (CT). Medical imaging research is being advanced by computer-aided diagnostic processes that enable visualization. Using clustering, automatic tumor segmentation leads to accurate tumor detection that reduces risk and helps with effective treatment. This study proposed a better Fuzzy C-Means segmentation algorithm for MRI images. To reduce complexity, the most relevant shape, texture, and color features are selected. The improved Extreme Learning machine classifies the tumors with 98.56% accuracy, 99.14% precision, and 99.25% recall. The proposed classifier consistently demonstrates higher accuracy across all tumor classes compared to existing models. Specifically, the proposed model exhibits accuracy improvements ranging from 1.21% to 6.23% when compared to other models. This consistent enhancement in accuracy emphasizes the robust performance of the proposed classifier, suggesting its potential for more accurate and reliable brain tumor classification. The improved algorithm achieved accuracy, precision, and recall rates of 98.47%, 98.59%, and 98.74% on the Fig share dataset and 99.42%, 99.75%, and 99.28% on the Kaggle dataset, respectively, which surpasses competing algorithms, particularly in detecting glioma grades. The proposed algorithm shows an improvement in accuracy, of approximately 5.39%, in the Fig share dataset and of 6.22% in the Kaggle dataset when compared to existing models. Despite challenges, including artifacts and computational complexity, the study\'s commitment to refining the technique and addressing limitations positions the improved FCM model as a noteworthy advancement in the realm of precise and efficient brain tumor identification.

Untethered micro/nanorobots (MNRs) show great promise in biomedicine. However, high-precision targeted in vivo navigation of MNRs into both deep and tiny microtube networks comes with big challenges because the present medical imaging cannot simultaneously meet the requirements of high resolution, high penetration depth, and high real-time performance. Inspired by intracellular motor proteins that transport cargo along cytoskeletal tracks, this study proposed a microtube inwall-guided targeted self-navigation strategy of magnetic microwheels (μ-wheels) that relies only on interactions with a microtube inwall, compared to conventional techniques that rely on real-time imaging and tracking of MNRs. By presetting the direction of the rotating magnetic field, the μ-wheel realized targeted navigation along the inwall. The propulsion principles behind it are elaborated. The targeted self-navigation of the μ-wheels in three-dimensional microtube networks, a spiral microtube, and an intrahepatic bile duct of a pig was conducted. Lastly, based on the strategy, a practical tumor early detection method was proposed and verified by means of magnetic resonance imaging. The microtube inwall-guided targeted self-navigation strategy reduces the dependence of in vivo targeted navigation of MNRs on the real-time performance of medical imaging technology and greatly contributes to the development of MNRs in biomedical applications.

OBJECTIVE: To investigate the feasibility of using deep learning to identify tumor-containing axial slices on breast MRI images.
METHODS: This IRB-approved retrospective study included consecutive patients with operable invasive breast cancer undergoing pretreatment breast MRI between January 1, 2014, and December 31, 2017. Axial tumor-containing slices from the first postcontrast phase were extracted. Each axial image was subdivided into two subimages: one of the ipsilateral cancer-containing breast and one of the contralateral healthy breast. Cases were randomly divided into training, validation, and testing sets. A convolutional neural network was trained to classify subimages into \"cancer\" and \"no cancer\" categories. Accuracy, sensitivity, and specificity of the classification system were determined using pathology as the reference standard. A two-reader study was performed to measure the time savings of the deep learning algorithm using descriptive statistics.
RESULTS: Two hundred and seventy-three patients with unilateral breast cancer met study criteria. On the held-out test set, accuracy of the deep learning system for tumor detection was 92.8% (648/706; 95% confidence interval: 89.7%-93.8%). Sensitivity and specificity were 89.5% and 94.3%, respectively. Readers spent 3 to 45 seconds to scroll to the tumor-containing slices without use of the deep learning algorithm.
CONCLUSIONS: In breast MR exams containing breast cancer, deep learning can be used to identify the tumor-containing slices. This technology may be integrated into the picture archiving and communication system to bypass scrolling when viewing stacked images, which can be helpful during nonsystematic image viewing, such as during interdisciplinary tumor board meetings.

IR780 iodide is a commercially available targeted near-infrared contrast agent for in vivo imaging and cancer photodynamic or photothermal therapy, whereas the accumulation, dynamics, and retention of IR780 in biological tissue, especially in tumor is still under-explored. Diffuse fluorescence tomography (DFT) can be used for localization and quantification of the three-dimensional distribution of NIR fluorophores. Herein, a homemade DFT imaging system combined with tumor-targeted IR780 was utilized for cancer imaging and pharmacokinetic evaluation. The aim of this study is to comprehensively assess the biochemical and pharmacokinetic characteristics of IR780 with the aid of DFT imaging. The optimal IR780 concentration (20 μg/mL) was achieved first. Subsequently, the good biocompatibility and cellar uptake of IR780 was demonstrated through the mouse acute toxic test and cell assay. In vivo, DFT imaging effectively identified various subcutaneous tumors and revealed the long-term retention of IR780 in tumors and rapid metabolism in the liver. Ex vivo imaging indicated IR780 was mainly concentrated in tumor and lung with significantly different from the distribution in other organs. DFT imaging allowed sensitive tumor detection and pharmacokinetic rates analysis. Simultaneously, the kinetics of IR780 in tumors and liver provided more valuable information for application and development of IR780.