Inter-reader agreement of LI-RADS treatment response algorithm among three readers with different seniorities for hepatocellular carcinoma after locoregional therapy.

BACKGROUND: The accurate evaluation of tumor response after locoregional therapy is crucial for adjusting therapeutic strategy and guiding individualized follow-up. PURPOSE: To determine the inter-reader agreement of the LR-TR algorithm for hepatocellular carcinoma treated with locoregional therapy among radiologists with different seniority. MATERIAL AND METHODS: A total of 275 treated observations on 249 MRI scans from 99 patients were retrospectively collected. Three readers of different seniorities (senior, intermediate, and junior with 10, 6, and 2 years of experience in hepatic imaging, respectively) analyzed the presence or absence of features (arterial-phase hyperenhancement and washout) and evaluated LR-TR category. RESULTS: There were substantial inter-reader agreements for overall LR-TR categorization (kappa = 0.704), LR-TR viable (kappa = 0.715), and LR-TR non-viable (kappa = 0.737), but fair inter-reader agreement for LR-TR equivocal (kappa = 0.231) among three readers. The inter-reader agreement was substantial for arterial-phase hyperenhancement (kappa = 0.725), but moderate for washout (kappa = 0.443) among three readers. The inter-reader agreements between two readers were substantial for overall LR-TR categorization (kappa = 0.734, 0.727, 0.652), LR-TR viable (kappa = 0.719, 0.752, 0.678), and LR-TR non-viable (kappa = 0.758, 0.760, 0.694), which were at the same level as the inter-reader agreements among three readers. In addition, the inter-reader agreements between two readers were substantial for arterial-phase hyperenhancement (kappa = 0.733, 0.766, 0.678), but moderate for washout (kappa = 0.473, 0.422, 0.446), which were at the same level as the inter-reader agreements among three readers. CONCLUSION: LR-TR algorithm demonstrated overall substantial inter-reader agreement among radiologists with different seniority.

CT-guided microcoil versus hook-wire localization of pulmonary nodule prior to video-assisted thoracoscopic surgery without fluoroscopic guidance.

BACKGROUND: Both microcoils and hook-wires are commonly utilized for preoperative pulmonary nodule localization due to their convenience, but it remains unclear which one should be prioritized for recommendation. AIMS: To compare the safety and efficacy of microcoils and hook-wires for pulmonary nodule localization. METHODS: From January 2021 to December 2021, 310 consecutive patients (113 males and 197 females) with 341 pulmonary nodules who underwent CT-guided microcoil or hook-wire localization prior to video-assisted thoracoscopic surgery (VATS) at our center were retrospectively included in this study. There were 161 patients in the microcoil group and 149 patients in the hook-wire group. The successful localization rate, complication rate, radiation exposure, and medical costs were compared between the two groups. RESULTS: A total of 341 pulmonary nodules were localized, with a success rate of 99% (180/184) in the microcoil group and 93% (146/157) in the hook-wire group, respectively. All patients successfully underwent VATS. Multivariate analysis revealed that hook-wire localization, shorter needle depth into the lung tissue and the longer waiting time from localization to VATS were the risk factors for the localization failure. The incidences of pneumothorax in the microcoil group and hook-wire group were 34.8% (56/161) and 34.9% (52/149), respectively (P = 0.983). The incidences of pneumorrhagia were 13% (24/184) and 46.5% (73/157), respectively (P = 0.000). Multivariate analysis revealed that hook-wire localization and greater depth of needle penetration into lung tissue were risk factors for pneumorrhagia. CONCLUSION: Microcoil localization of pulmonary nodules is superior to hook-wire localization in terms of efficacy and safety. This finding provides insight into priority and broader promotion of microcoil localization.

Development and external validation of a machine-learning based model to predict pre-sarcopenia in MASLD population: Results from NHANES 2017-2018.

INTRODUCTION AND OBJECTIVES: With rising prevalence of pre-sarcopenia in metabolic dysfunction-associated steatotic liver disease (MASLD), this study aimed to develop and validate machine learning-based model to identify pre-sarcopenia in MASLD population. MATERIALS AND METHODS: A total of 571 MASLD subjects were screened from the National Health and Nutrition Examination Survey 2017-2018. This cohort was randomly divided into training set and internal testing set with a ratio of 7:3. Sixty-six MASLD subjects were collected from our institution as external validation set. Four binary classifiers, including Random Forest (RF), support vector machine, and extreme gradient boosting and logistic regression, were fitted to identify pre-sarcopenia. The best-performing model was further validated in external validation set. Model performance was assessed in terms of discrimination and calibration. Shapley Additive explanations were used for model interpretability. RESULTS: The pre-sarcopenia rate was 17.51 % and 15.16 % in NHANES cohort and external validation set, respectively. RF outperformed other models with area under receiver operating characteristic curve (AUROC) of 0.819 (95 %CI: 0.749, 0.889). When six top-ranking features were retained as per variable importance, including weight-adjusted waist, sex, race, creatinine, education and alkaline phosphatase, a final RF model reached an AUROC being 0.824 (0.737, 0.910) and 0.732 (95 %CI: 0.529, 0.936) in internal and external validation sets, respectively. The model robustness was proved in sensitivity analysis. The calibration curve and decision curve analysis confirmed a good calibration capacity and good clinical usage. CONCLUSIONS: This study proposed a user-friendly model using explainable machine learning algorithm to predict pre-sarcopenia in MASLD population. A web-based tool was provided to screening pre-sarcopenia in community and hospitalization settings.

OOD-CV-v2 : An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images.

Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors. We introduce OOD-CV-v2, a benchmark dataset that includes out-of-distribution examples of 10 object categories in terms of pose, shape, texture, context and the weather conditions, and enables benchmarking of models for image classification, object detection, and 3D pose estimation. In addition to this novel dataset, we contribute extensive experiments using popular baseline methods, which reveal that: 1) Some nuisance factors have a much stronger negative effect on the performance compared to others, also depending on the vision task. 2) Current approaches to enhance robustness have only marginal effects, and can even reduce robustness. 3) We do not observe significant differences between convolutional and transformer architectures. We believe our dataset provides a rich test bed to study robustness and will help push forward research in this area. Our dataset is publically available online, https://genintel.mpi-inf.mpg.de/ood-cv-v2.html.

Screening of identification algorithm for rodent-induced bare patches based on the drone imagery.

Rodent-infested bald spots are crucial indicators of rodent infestation in grasslands. Leveraging Unmanned Aerial Vehicle (UAV) remote sensing technology for discerning detrimental bald spots among plateau pikas has significant implications for assessing associated ecological hazards. Based on UAV-visible light imagery, we classified and recognized the characteristics of plateau pika habitats with five supervised classification algorithms, i.e., minimum distance classification (MinD), maximum likelihood classification (ML), support vector machine classification (SVM), Mahalanobis distance classification (MD), and neural network classification (NN) . The accuracy of the five methods was evaluated using a confusion matrix. Results showed that NN and SVM exhibited superior performance than other methods in identifying and classifying features indicative of plateau pika habitats. The mapping accuracy of NN for grassland and bald spots was 98.1% and 98.5%, respectively, with corresponding user accuracy was 98.8% and 97.7%. The overall model accuracy was 98.3%, with a Kappa coefficient of 0.97, reflecting minimal misclassification and omission errors. Through practical verification, NN exhibited good stability. In conclusion, the neural network method was suitable for identifying rodent-damaged bald spots within alpine meadows.

Accelerated proton dissociation in an excited state induces superacidic microenvironments around graphene quantum dots.

Investigating proton transport at the interface in an excited state facilitates the mechanistic investigation and utilization of nanomaterials. However, there is a lack of suitable tools for in-situ and interfacial analysis. Here we addresses this gap by in-situ observing the proton transport of graphene quantum dots (GQDs) in an excited state through reduction of magnetic resonance relaxation time. Experimental results, utilizing 0.1 mT ultra-low-field nuclear magnetic resonance relaxometry compatible with a light source, reveal the light-induced proton dissociation and acidity of GQDs' microenvironment in the excited state (Hammett acidity function: -13.40). Theoretical calculations demonstrate significant acidity enhancement in -OH functionalized GQDs with light induction ( p K a * = -4.62, stronger than that of H2SO4). Simulations highlight the contributions of edge and phenolic -OH groups to proton dissociation. The light-induced superacidic microenvironment of GQDs benefits functionalization and improves the catalytic performances of GQDs. Importantly, this work advances the understanding of interfacial properties of light-induced sp2-sp3 carbon nanostructure and provides a valuable tool for exploring catalyst interfaces in photocatalysis.

Hybrid ant colony-based inter-cluster routing protocol for FANET.

This study addresses the challenges in large-scale unmanned aerial vehicle (UAV) clusters, specifically the scalability issues and limitations of using reactive routing protocols for inter-cluster routing. These traditional methods place an excessive burden on cluster heads and struggle to adapt to frequently changing topologies, leading to decreased network performance. To solve these problems, we propose an innovative inter-cluster routing protocol (ICRP), which is based on a hybrid ant colony algorithm. During the route establishment phase, ICRP uses this algorithm to identify the optimal relay node. This approach is inspired by the foraging behavior of Physarum polycephalum, combining factors such as the number of hops from the source node, the load condition of the node, and its weight in the pheromone calculation. In the route maintenance phase, ICRP uses a predictive repair and contraction mechanism to dynamically maintain routes, accommodating the high mobility of UAVs. Comparative simulations in OMNeT + + showed that this protocol surpasses ad-hoc on-demand distance vector (AODV), fuzzy-logic-assisted-AODV, and Enhanced-Ant-AODV routing protocols in packet delivery rate and end-to-end transmission delay. Furthermore, it showed superior adaptation to network environments with high-speed node mobility.

The Balloon Catheter Method and the End-hole Catheter Method in the Measurement of Hepatic Venous Pressure Gradient: a Comparative Study.

OBJECTIVE: This study aims to evaluate the differences between The balloon catheter method and End-hole Catheter Method in measuring hepatic venous pressure gradient (HVPG) among cirrhosis patients. METHODS: From October 2017 to January 2024, patients who underwent HVPG measurements using both methods were consecutively included. HVPGs obtained from both methods were compared with the portal vein pressure gradient (PPG) obtained via transjugular intrahepatic portosystemic shunt (TIPS) using paired comparisons. Additionally, the consistency and predictive ability for bleeding risk of the two methods, as well as the impact of intrahepatic veno-venous shunt (IHVS), were analyzed. RESULTS: The study enrolled 145 patients, each of whom had HVPG measured by both methods. PPG was measured in 61 patients. There was a statistically significant difference between the PPGs and HVPGs measured by both the balloon catheter method and the end-hole catheter method (P < 0.001), with the HVPG mean values obtained by the end-hole catheter method being closer to the PPGs. In the non-IHVS group, no significant statistical difference was found between the two methods (P = 0.071). In contrast, the IHVS group showed a significant difference (P < 0.001), with a mean difference of 2.98 ± 4.03 mmHg. When IHVS was absent, the measurement results from the end-hole catheter method and the balloon catheter method were found to be highly correlated. The end-hole catheter method has a higher screening capability for patients at risk of bleeding compared to the balloon catheter method (75.90% vs. 72.86%). CONCLUSION: HVPG measurements using either the balloon catheter method or end-hole catheter method showed significant difference with the PPG. The end-hole catheter method has a higher screening capability for patients at risk of bleeding, and IHVS could lead to lower HVPG measurements with The balloon catheter method.

Nitrogen-Doped 3D-Graphene Advances Near-Infrared Photodetector for Logic Circuits and Image Sensors Overcoming 2D Limitations.

The limitations of two-dimensional (2D) graphene in broadband photodetector are overcome by integrating nitrogen (N) doping into three-dimensional (3D) structures within silicon (Si) via plasma-assisted chemical vapor deposition (PACVD) technology. This contributes to the construction of vertical Schottky heterojunction broad-spectrum photodetectors and applications in logic devices and image sensors. The natural nanoscale resonant cavity structure of 3D-graphene enhances photon capture efficiency, thereby increasing photocarrier generation. N-doping can fine-tune the electronic structure, advancing the Schottky barrier height and reducing dark current. The as-fabricated photodetector exhibits exceptional self-driven photoresponse, especially at 1550 nm, with an excellent photoresponsivity (79.6 A/W), specific detectivity (1013 Jones), and rapid response of 130 µs. Moreover, it enables logic circuits, high-resolution pattern image recognition, and broadband spectra recording across the visible to near-infrared range (400-1550 nm). This research will provide new views and technical support for the development and widespread application of high-performance semiconductor-based graphene broadband detectors.

A Tape-Wrapping Strategy towards Electrochemical Fabrication of Water-Dispersible Graphene.

Graphene has achieved mass production via various preparative routes and demonstrated its uniqueness in many application fields for its intrinsically high electron mobility and thermal conductivity. However, graphene faces limitations in assembling macroscopic structures because of its hydrophobic property. Therefore, balancing high crystal quality and good aqueous dispersibility is of great importance in practical applications. Herein, we propose a tape-wrapping strategy to electrochemically fabricate water-dispersible graphene (w-Gr) with both excellent dispersibility (~4.5 mg/mL, stable over 2 months), and well-preserved crystalline structure. A large production rate (4.5 mg/min, six times faster than previous electrochemical methods), high yield (65.4% ≤5 atomic layers) and good processability are demonstrated. A mechanism investigation indicates that the rational design of anode configuration to ensure proper oxidation, deep exfoliation and unobstructed mass transfer is responsible for the high efficiency of this strategy. This simple yet efficient electrochemical method is expected to promote the scalable preparation and applications of graphene.

A novel method for quantitative determination of multiple substances using Raman spectroscopy combined with CWT.

The identification of mixed solutions is a challenging and important subject in chemical analysis. In this paper, we propose a novel workflow that enables rapid qualitative and quantitative detection of mixed solutions. We use a methanol-ethanol mixed solution as an example to demonstrate the superiority of this workflow. The workflow includes the following steps: (1) converting Raman spectra into Raman images through CWT; (2) using MobileNetV3 as the backbone network, improved multi-label and multi-channel synchronization enables simultaneous prediction of multiple mixture concentrations; and (3) using transfer learning and multi-stage training strategies for training to achieve accurate quantitative analysis. We compare six traditional machine learning algorithms and two deep learning models to evaluate the performance of our new method. The experimental results show that our model has achieved good prediction results when predicting the concentration of methanol and ethanol, and the coefficient of determination R2 is greater than 0.999. At different concentrations, both MAPE and RSD outperform other models, which demonstrates that our workflow has outstanding analytical capabilities. Importantly, we have solved the problem that current quantitative analysis algorithms for Raman spectroscopy are almost unable to accurately predict the concentration of multiple substances simultaneously. In conclusion, it is foreseeable that this non-destructive, automated, and highly accurate workflow can further advance Raman spectroscopy.

Rationale for immune checkpoint inhibitors plus targeted therapy for advanced renal cell carcinoma.

Renal cell carcinoma (RCC) is a frequent urological malignancy characterized by a high rate of metastasis and lethality. The treatment strategy for advanced RCC has moved through multiple iterations over the past three decades. Initially, cytokine treatment was the only systemic treatment option for patients with RCC. With the development of medicine, antiangiogenic agents targeting vascular endothelial growth factor and mammalian target of rapamycin and immunotherapy, immune checkpoint inhibitors (ICIs) have emerged and received several achievements in the therapeutics of advanced RCC. However, ICIs have still not brought completely satisfactory results due to drug resistance and undesirable side effects. For the past years, the interests form researchers have been attracted by the combination of ICIs and targeted therapy for advanced RCC and the angiogenesis and immunogenic tumor microenvironmental variations in RCC. Therefore, we emphasize the potential principle and the clinical progress of ICIs combined with targeted treatment of advanced RCC, and summarize the future direction.

Deep learning-based Raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach.

Raman spectroscopy is a general and non-destructive detection technique that can obtain detailed information of the chemical structure of materials. In the past, when using chemometric algorithms to analyze the Raman spectra of mixtures, the challenges of complex spectral overlap and noise often limited the accurate identification of components. The emergence of deep learning has introduced a novel approach to qualitative analysis of mixed Raman spectra. In this paper, we propose a deep learning-based Raman spectroscopy qualitative analysis algorithm (RST) by borrowing the ideas of convolutional neural network and Transformer. By transforming the Raman spectrum into 64 word vectors, the contribution weights of each word vector to the components are obtained. For the 75 spectral data used for validation, the positive identification rate can reach 100.00 %, the recall rate can reach 99.3 %, the average identification score can reach 9.51, and it is applicable to the fields of Raman and surface-enhanced Raman spectroscopy. Furthermore, compared with traditional CNN models, RST has excellent accuracy and robustness in identifying components in complex mixtures. The model's interpretability has been enhanced, aiding in a deeper understanding of spectroscopic learning patterns for future analysis of more complex mixtures.

Deep learning-based arterial subtraction images improve the detection of LR-TR algorithm for viable HCC on extracellular agents-enhanced MRI.

PURPOSE: To determine the role of deep learning-based arterial subtraction images in viability assessment on extracellular agents-enhanced MRI using LR-TR algorithm. METHODS: Patients diagnosed with HCC who underwent locoregional therapy were retrospectively collected. We constructed a deep learning-based subtraction model and automatically generated arterial subtraction images. Two radiologists evaluated LR-TR category on ordinary images and then evaluated again on ordinary images plus arterial subtraction images after a 2-month washout period. The reference standard for viability was tumor stain on the digital subtraction hepatic angiography within 1 month after MRI. RESULTS: 286 observations of 105 patients were ultimately enrolled. 157 observations were viable and 129 observations were nonviable according to the reference standard. The sensitivity and accuracy of LR-TR algorithm for detecting viable HCC significantly increased with the application of arterial subtraction images (87.9% vs. 67.5%, p < 0.001; 86.4% vs. 75.9%, p < 0.001). And the specificity slightly decreased without significant difference when the arterial subtraction images were added (84.5% vs. 86.0%, p = 0.687). The AUC of LR-TR algorithm significantly increased with the addition of arterial subtraction images (0.862 vs. 0.768, p < 0.001). The arterial subtraction images also improved inter-reader agreement (0.857 vs. 0.727). CONCLUSION: Extended application of deep learning-based arterial subtraction images on extracellular agents-enhanced MRI can increase the sensitivity of LR-TR algorithm for detecting viable HCC without significant change in specificity.

Symmetry-Triggered Tunable Phosphorescence Lifetime of Graphene Quantum Dots in a Solid State.

Studying the phosphorescent mechanisms of carbon nanostructures synthesized by the "bottom-up" approach is key to understanding the structure modulation and the interfacial properties of carbon nanostructures. In this work, the relationships among symmetry of precursors in the "bottom-up" synthesis, structures of products, and phosphorescence lifetimes of graphene quantum dots (GQDs) are studied. The symmetry matching of precursors in the formation of a D6h graphene-like framework is considered the key factor in controlling the separability of sp2 domains in GQDs. As the separability of sp2 domains in GQDs increases, the phosphorescence lifetimes (14.8-125.5 ms) of GQDs in the solid state can be tuned. Machine learning is used to define the degree of disorder (S) of the GQD structure, which quantitatively describes the different space groups of precursors. The negative correlation between S and the oscillator strength of GQDs is uncovered. Therefore, S can be recognized as reflective of oscillator strength in the GQD structure. Finally, based on the correlations found between the structures and phosphorescence lifetimes of GQDs, GQDs with an ultralong phosphorescence lifetime (28.5 s) are obtained. Moreover, GQDs with visible phosphorescence emission (435-618 nm) are synthesized.

The Synergistic Effect on the Mimetic Optical Structure of Feline Eyes toward Household Health Monitoring of Acute and Chronic Diseases.

A breakthrough in the performance of bionic optical structures will only be achieved if we can obtain an in-depth understanding of the synergy mechanisms operating in natural optical structures and find ways to imitate them. In this work, inspired by feline eyes, an optical substrate that takes advantage of a synergistic effect that occurs between resonant and reflective structures was designed. The synergistic effect between the reflective and resonant components leads to a Raman enhancement factor (EF) of 1.16 × 107, which is much greater than that achieved using the reflective/resonant cavities on their own. Finite-difference time-domain (FDTD) simulations and experimental results together confirm that the mechanism of this synergistic effect is achieved by realizing multiple reflections and repeated absorptions of light, generating a strong local electric field. Thus, a 2-3 order of magnitude increase in sensitivity could be achieved. More importantly, with the homemade centrifugal device, above optical substrates were further used to develop a rapidly highly sensitive household health monitoring system (detection time <3 min). It can thus be used to give early warning of acute diseases with high risk (e.g., acute myocardial infarction (AMI) and cerebral peduncle). Due to the good reusability and storability (9% and 8% reduction in EF after washing 30 times and 9 months of storage, respectively) of the substrates, the substrates thus reduce detection costs (to ∼$1), making them much cheaper to use than the current gold-standard methods (e.g., ∼$16 for gout detection).

Correlation between computed tomography-based body composition parameters and hepatic venous pressure gradient in patients with cirrhosis: a systematic review and meta-analysis.

PURPOSE: Computed tomography (CT)-based body composition parameters and the hepatic venous pressure gradient (HVPG) are key characteristics in patients with liver cirrhosis. The present study aims to explore the correlation between CT-based body composition parameters and HVPG, as well as the difference in HVPG between patients with and patients without sarcopenia. METHODS: A literature search for studies reporting the correlation between HVPG and CT-based body composition parameters published in English up to August 2023 in four databases, Embase, MEDLINE (via PubMed), Web of Science, and Cochrane Library, was conducted. The correlation coefficient between HVPG and CT-based body composition parameters was the primary outcome, and the difference in the HVPG value between the sarcopenia and non-sarcopenia groups was the secondary outcome. A meta-analysis was conducted using a random-effects models. The methodologic quality was assessed using the Quality Assessment of Diagnostic Studies instrument. RESULTS: A total of 652 articles were identified, of which nine studies (n = 1,569) met the eligibility criteria. Among them, seven studies reported the primary outcome via the muscle index, five via the skeletal muscle index (SMI), two via the psoas-muscle-related index (PRI), and three via two adipose tissue indexes. A total of five studies reported the secondary outcome: four via SMI and one via PRI. No evidence of a significant correlation was determined between the various body composition parameters and the HVPG value, either in the muscle index or the adipose tissue index. Higher HVPG values were observed in patients with sarcopenia than in patients without sarcopenia [pooled standardized mean difference (SMD): 0.628 (-0.350, 1.606), P < 0.001; I2 = 92.8%; P < 0.001] when an Asian sarcopenia definition was adopted. In contrast, when a Western cut-off value was applied, the HVPG value was higher in patients without sarcopenia than in patients with sarcopenia [pooled SMD: -0.201 (-0.366, -0.037), P = 0.016; I2 = 0.00%; P = 0.785]. CONCLUSION: No sufficient evidence regarding a correlation between the CT-based body composition and HVPG value was discovered. The difference in the HVPG value between the sarcopenia and non-sarcopenia groups was likely dependent on the sarcopenic cut-off value.

Fast proton transport enables the magnetic relaxation response of graphene quantum dots for monitoring the oxidative environment in vivo.

A magnetic relaxation switch (MRS) that targets small molecules such as H2O2 is difficult to realize because of the small size of the targets, which cannot gather enough MRS probes to form aggregates and generate a difference in magnetic relaxation times. Therefore, the development of small molecule-targeted MRS is strongly dependent on changes in the interfacial structure of the probe, which modulates the proton transport behavior near the probe. Herein, functionalized graphene quantum dots (GQDs) consisting of GQDs with disulfide bonds, polyethylene glycol (PEG), and paramagnetic Gd3+ were used as the MRS probe to sense H2O2. The structure of GQDs changed after reacting with H2O2. The PEG assembled a tube for transmitting changes in GQDs via proton transport and thus enabled the magnetic relaxation response of the probe towards H2O2. Pentaethylene glycol was experimentally and theoretically proven to have the strongest ability to transport protons. Such a probe can be applied in the differentiation of healthy and senescent cells/tissues using in vitro fluorescent imaging and in vivo magnetic resonance imaging. This work provides a reliable solution for building a proton transport route, which not only enables the response of the MRS probe towards the targets but also demonstrates the design of carbon nanostructures with proton transport behaviors.

Tumor diagnosis using carbon-based quantum dots: Detection based on the hallmarks of cancer.

Carbon-based quantum dots (CQDs) have been shown to have promising application value in tumor diagnosis. Their use, however, is severely hindered by the complicated nature of the nanostructures in the CQDs. Furthermore, it seems impossible to formulate the mechanisms involved using the inadequate theoretical frameworks that are currently available for CQDs. In this review, we re-consider the structure-property relationships of CQDs and summarize the current state of development of CQDs-based tumor diagnosis based on biological theories that are fully developed. The advantages and deficiencies of recent research on CQDs-based tumor diagnosis are thus explained in terms of the manifestation of nine essential changes in cell physiology. This review makes significant progress in addressing related problems encountered with other nanomaterials.