Multi-Biomarker Profiling for Precision Diagnosis of Lung Cancer.

Multi-biomarker analysis can enhance the accuracy of the single-biomarker analysis by reducing the errors caused by genetic and environmental differences. For this reason, multi-biomarker analysis shows higher accuracy in early and precision diagnosis. However, conventional analysis methods have limitations for multi-biomarker analysis because of their long pre-processing times, inconsistent results, and large sample requirements. To solve these, a fast and accurate precision diagnostic method is introduced for lung cancer by multi-biomarker profiling using a single drop of blood. For this, surface-enhanced Raman spectroscopic immunoassay (SERSIA) is employed for the accurate, quick, and reliable quantification of biomarkers. Then, it is checked the statistical relation of the multi-biomarkers to differentiate between healthy controls and lung cancer patients. This approach has proven effective; with 20 µL of blood serum, lung cancer is diagnosed with 92% accuracy. It also accurately identifies the type and stage of cancer with 87% and 85%, respectively. These results show the importance of multi-biomarker analysis in overcoming the challenges posed by single-biomarker diagnostics. Furthermore, it markedly improves multi-biomarker-based analysis methods, illustrating its important impact on clinical diagnostics.

Needle-Shaped Biosensors for Precision Diagnoses: From Benchtop Development to In Vitro and In Vivo Applications.

To achieve the accurate recognition of biomarkers or pathological characteristics within tissues or cells, in situ detection using biosensor technology offers crucial insights into the nature, stage, and progression of diseases, paving the way for enhanced precision in diagnostic approaches and treatment strategies. The implementation of needle-shaped biosensors (N-biosensors) presents a highly promising method for conducting in situ measurements of clinical biomarkers in various organs, such as in the brain or spinal cord. Previous studies have highlighted the excellent performance of different N-biosensor designs in detecting biomarkers from clinical samples in vitro. Recent preclinical in vivo studies have also shown significant progress in the clinical translation of N-biosensor technology for in situ biomarker detection, enabling highly accurate diagnoses for cancer, diabetes, and infectious diseases. This article begins with an overview of current state-of-the-art benchtop N-biosensor designs, discusses their preclinical applications for sensitive diagnoses, and concludes by exploring the challenges and potential avenues for next-generation N-biosensor technology.

Shaping the future of gastrointestinal cancers through metabolic interactions with host gut microbiota.

Gastrointestinal (GI) cancers represent a significant global health challenge, driving relentless efforts to identify innovative diagnostic and therapeutic approaches. Recent strides in microbiome research have unveiled a previously underestimated dimension of cancer progression that revolves around the intricate metabolic interplay between GI cancers and the host's gut microbiota. This review aims to provide a comprehensive overview of these emerging metabolic interactions and their potential to catalyze a paradigm shift in precision diagnosis and therapeutic breakthroughs in GI cancers. The article underscores the groundbreaking impact of microbiome research on oncology by delving into the symbiotic connection between host metabolism and the gut microbiota. It offers valuable insights into tailoring treatment strategies to individual patients, thus moving beyond the traditional one-size-fits-all approach. This review also sheds light on novel diagnostic methodologies that could transform the early detection of GI cancers, potentially leading to more favorable patient outcomes. In conclusion, exploring the metabolic interactions between host gut microbiota and GI cancers showcases a promising frontier in the ongoing battle against these formidable diseases. By comprehending and harnessing the microbiome's influence, the future of precision diagnosis and therapeutic innovation for GI cancers appears more optimistic, opening doors to tailored treatments and enhanced diagnostic precision.

Comparative Immunohistochemical Analysis of Clinicopathological Subgroups in Hepatocellular Carcinomas from Japan and Indonesia.

Background: Standardized pathological evaluation based on immunohistochemical (IHC) analysis could improve hepatocellular carcinoma (HCC) diagnoses worldwide. We evaluated differences in clinicopathological subgroups in HCCs from two academic institutions in Tokyo-Japan, and Jakarta-Indonesia. Methods: Clinicopathological parameters and molecular expression patterns were evaluated in 35 HCCs from Indonesia and 41 HCCs from Japan. IHC analysis of biliary/stem cell (B/S) markers (cytokeratin 19, sal-like protein 4, epithelial cell adhesion molecule) and Wnt/ß-catenin (W/B) signaling-related molecules (ß-catenin, glutamine synthetase) could determine the IHC-based subgroups. For immuno-subtypes categorization, CD3/CD79α double immunohistochemistry was done to evaluate the infiltration of T and B cells. CD34 staining allowed identification of vessels that encapsulated tumor clusters (VETC). Results: Indonesian HCC patients were mostly <60 years old (66%) with a hepatitis B virus (HBV) background (82%), in contrast to Japanese HCC patients (8% and 19%, respectively, both P < 0.001). In comparison with Japanese, Indonesian cases more frequently had >5 cm tumor size (74% vs 23%, P = 0.001), poor differentiation (40% vs 24%), portal vein invasion (80% vs 61%), and α-fetoprotein levels >500 ng/ml (45% vs 13%, P = 0.005). No significant differences were found in the proportions of B/S, W/B, and -/- subgroups from both countries. No immune-high tumors were observed among Indonesian cases, and immune-low tumors (66%) were more common than in Japanese cases (54%). VETC-positive tumors in Indonesia were significantly more common (29%), and most were in the HBV (90%) and -/- subgroups (90%), whereas Japanese VETC cases (10%, P = 0.030) were nonviral (100%) and W/B subgroups (75%). Conclusion: IHC-based analysis more precisely reflected the clinicopathological differences of HCCs in Japan and Indonesia. These findings provide new insights into standardization attempts and HCC heterogeneity among countries.

Long-read sequencing solves complex structure of CYP21A2 in a large 21-hydroxylase deficiency cohort.

CONTEXT: Genetic testing for 21-hydroxylase deficiency (21-OHD) is always challenging. Current approaches, short-read sequencing and multiplex ligation-dependent probe amplification (MLPA), are insufficient for the detection of chimeric genes or complicated variants from multiple copies. Recently developed long-read sequencing (LRS) can solve this problem. OBJECTIVE: To investigate the clinical utility of LRS in precision diagnosis of 21-hydroxylase deficiency. METHODS: In the cohort of 832 patients with 21-OHD, the current approaches provided the precise molecular diagnosis for 81.7% (680/832) of cases. LRS was performed to solve the remaining 144 cases with complex chimeric variants and eight cases with variants from multiple copies. Clinical manifestations in patients with continuous deletions of CYP21A2 extending to TNXB (namely CAH-X) were further evaluated. RESULTS: Using LRS in combination with previous genetic test results, a total of 16.9% (281/1664) CYP21A1P/CYP21A2 or TNXA/TNXB chimeric alleles were identified in 832 patients, with CYP21A1P/CYP21A2 accounting for 10.4% and TNXA/TNXB for 6.5%. The top three common chimeras were CYP21 CH-1, TNX CH-1 and TNX CH-2, accounting for 77.2% (217/281) of all chimeric alleles. The eight patients with variants on multiple copies of CYP21A2 were accurately identified with LRS. The prevalence of CAH-X in our cohort was 12.1%, and a high frequency of connective tissue-related symptoms was observed in CAH-X patients. CONCLUSION: LRS can detect all types of CYP21A2 variants, including complex chimeras and pathogenic variants on multiple copies in patients with 21-OHD, which could be utilized as a first-tier routine test for the precision diagnosis and categorization of congenital adrenal hyperplasia.

Potential of niacin skin flush response in adolescent depression identification and severity assessment: a case-control study.

BACKGROUND: The diagnosis of adolescent Depressive Disorder (DD) lacks specific biomarkers, posing significant challenges. This study investigates the potential of Niacin Skin Flush Response (NSFR) as a biomarker for identifying and assessing the severity of adolescent Depressive Disorder, as well as distinguishing it from Behavioral and Emotional Disorders typically emerging in childhood and adolescence(BED). METHODS: In a case-control study involving 196 adolescents, including 128 Depressive Disorder, 32 Behavioral and Emotional Disorders, and 36 healthy controls (HCs), NSFR was assessed. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9) and anxious symptoms with the Generalized Anxiety Disorder 7-item scale (GAD-7). Pearson correlation analysis determined the relationships between NSFR and the severity of depression in DD patients. Receiver Operating Characteristic (ROC) was used to identify DD from BED integrating NSFR data with clinical symptom measures. RESULTS: The adolescent Depressive Disorder group exhibited a higher rate of severe blunted NSFR (21.4%) compared to BED (12.5%) and HC ( 8.3%). Adolescent Depressive Disorder with psychotic symptoms showed a significant increase in blunted NSFR (p = 0.016). NSFR had negative correlations with depressive (r = -0.240, p = 0.006) and anxious (r = -0.2, p = 0.023) symptoms in adolescent Depressive Disorder. Integrating NSFR with three clinical scales improved the differentiation between adolescent Depressive Disorder and BED (AUC increased from 0.694 to 0.712). CONCLUSION: The NSFR demonstrates potential as an objective biomarker for adolescent Depressive Disorder, aiding in screening, assessing severity, and enhancing insights into its pathophysiology and diagnostic precision.

Pulmonary delivery of nano-particles for lung cancer diagnosis and therapy: Recent advances and future prospects.

Although our understanding of lung cancer has significantly improved in the past decade, it is still a disease with a high incidence and mortality rate. The key reason is that the efficacy of the therapeutic drugs is limited, mainly due to insufficient doses of drugs delivered to the lungs. To achieve precise lung cancer diagnosis and treatment, nano-particles (NPs) pulmonary delivery techniques have attracted much attention and facilitate the exploration of the potential of those in inhalable NPs targeting tumor lesions. Since the therapeutic research focusing on pulmonary delivery NPs has rapidly developed and evolved substantially, this review will mainly discuss the current developments of pulmonary delivery NPs for precision lung cancer diagnosis and therapy. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Respiratory Disease Therapeutic Approaches and Drug Discovery > Emerging Technologies Diagnostic Tools > In Vivo Nanodiagnostics and Imaging.

Precision diagnosis and treatment for hematological malignancies / 陆军军医大学学报

With the in-depth research on the pathogenesis of hematological malignancies,precision diagnosis and stratified treatment have been continuously enhanced and optimized over the years.In recent years,with the aid of artificial intelligence,the diagnosis of the malignancies has been upgraded from manual and empirical to intelligent and mechanized,and it is expected to realize intelligent diagnosis with high efficiency and high accuracy in the future.At the same time,the evolution of small molecule drugs,antibody drugs,immunotherapy and hematopoietic stem cell transplantation,and the emergence of new drugs expand the treatment options for hematological malignancies.In this article,we review the precision diagnosis and treatment of hematological tumors from 3 aspects:precision/intelligent diagnosis,medication,and cellular therapy.

Genetic Testing Enhances the Precision Diagnosis and Treatment of Breast Cancer.

The contemporary comprehension of breast cancer has progressed to the molecular level. As a heterogeneous malignancy, conventional pathological diagnosis and histological classification could no longer meet the needs of precisely managing breast cancer. Genetic testing based on gene expression profiles and gene mutations has emerged and substantially contributed to the precise diagnosis and treatment of breast cancer. Multigene assays (MGAs) are explored for early-stage breast cancer patients, aiding the selection of adjuvant therapy and predicting prognosis. For metastatic breast cancer patients, testing specific genes indicates potentially effective antitumor agents. In this review, genetic testing in early-stage and metastatic breast cancer is summarized, as well as the advantages and challenges of genetic testing in breast cancer.

Progress of near-infrared-II fluorescence in precision diagnosis and treatment of colorectal cancer.

Colorectal cancer is a malignant tumour with high incidence and mortality worldwide; therefore, improving the early diagnosis of colorectal cancer and implementing a targeted "individualized treatment" strategy is of great concern. NIR-II fluorescence imaging is a large-depth, high-resolution optical bioimaging tool. Around the NIR-II window, researchers have developed a variety of luminescent probes, imaging systems, and treatment methods with colorectal cancer targeting capabilities, which can be visualized and image-guided in clinical surgery. This article aims to overcome the difficulties in diagnosing and treating colorectal cancer. The present review summarizes the latest results on using NIR-II fluorescence for targeted colorectal cancer imaging, expounds on the application prospects of NIR-II optical imaging for colorectal cancer, and discusses the imaging-guided multifunctional diagnosis and treatment platforms.

The Diagnostic Cockpit of the Future.

The integration of digital data from imaging, pathology, genomics, and other fields creates an opportunity to produce information-rich diagnoses, especially for complex patients. Technology exists today to create a "diagnostic cockpit" that can accept inputs from multiple sources, analyze them using artificial intelligence and other quantitative tools, and produce a precision diagnosis. Although barriers to creation and dissemination of such a cockpit exist, the metaphor provides a glimpse into the diagnostic processes of the future.

Extracapsular extension of transitional zone prostate cancer miss-detected by multiparametric magnetic resonance imaging.

OBJECTIVES: To demonstrate the importance of extracapsular extension (ECE) of transitional zone (TZ) prostate cancer (PCa), examine the causes of its missed detection by Mp-MRI, and develop a new predictive model by integrating multi-level clinical variables. MATERIALS AND METHODS: This retrospective study included 304 patients who underwent laparoscopic radical prostatectomy after 12 + X needle transperineal transrectal ultrasound (TRUS)-MRI-guided targeted prostate biopsy from 2018 to 2021 in our center was performed. RESULTS: In this study, the incidence rates of ECE were similar in patients with MRI lesions in the peripheral zone (PZ) and TZ (P = 0.66). However, the missed detection rate was higher in patients with TZ lesions than in those with PZ lesions (P < 0.05). These missed detections result in a higher positive surgical margin rate (P < 0.05). In patients with TZ lesions, detected MP-MRI ECE may have grey areas: the longest diameters of the MRI lesions were 16.5-23.5 mm; MRI lesion volumes were 0.63-2.51 ml; MRI lesion volume ratios were 2.75-8.86%; PSA were 13.85-23.05 ng/ml. LASSO regression was used to construct a clinical prediction model for predicting the risk of ECE in TZ lesions from the perspective of MRI and clinical features, including four variables: the longest diameter of MRI lesions, TZ pseudocapsule invasion, ISUP grading of biopsy pathology, and number of positive biopsy needles. CONCLUSIONS: Patients with MRI lesions in the TZ have the same incidence of ECE as those with lesions in the PZ, but a higher missed detection rate.

The Role of Epigenetics in Brain and Spinal Cord Tumors.

Identification of distinct genetic and epigenetic profiles in various neuroepithelial tumors has improved the classification and uncovered novel diagnostic, prognostic, and predictive molecular biomarkers for improved prediction of treatment response and outcome. Especially, in pediatric high-grade brain tumors, such as diffuse midline glioma, H3K27M-altered and posterior fossa group A-ependymoma, epigenetic changes predominate, along with changes in expression of known oncogenes and tumor suppressor genes induced by histone modifications and DNA methylation. The precise role of epigenetic abnormalities is important for understanding tumorigenesis and the establishment of brain tumor treatment strategies. Using powerful epigenetic-based therapies for cancer cells, the aberrantly regulated epigenome can be restored to a more normal state through epigenetic reprogramming. Combinations of agents targeting DNA methylation and/or other epigenetic modifications may be a promising cancer treatment. Therefore, the integration of multi-omics data including epigenomics is now important for classifying primary brain tumors and predicting their biological behavior. Recent advances in molecular genetics and epigenetic integrated diagnostics of brain tumors influence new strategies for targeted therapy.

Analysis of personalized precision diagnosis of 156 children with suspected rare inherited metabolic diseases / 中国热带医学

@#Abstract: Objective　To explore the accurate diagnosis of children with suspected rare inherited metabolic diseases, and to compare the application value of mass spectrometry and genetic testing in the diagnosis of rare inherited metabolic diseases (IMD). Methods　The clinical information, mass spectrometry, and genetic results of children with suspected rare inherited metabolic diseases admitted to the Department of Pediatrics, the Affiliated Haikou Hospital of Xiangya Medical College, Central South University from March 2017 to December 2021 were analyzed retrospectively. Results　156 children with suspected rare inherited metabolic diseases were detected by mass spectrometry, 67 cases were positive and 89 cases were negative. Children with positive initial examination were retested, and 19 cases were positive. Among the retest positive cases, 13 cases were given genetic testing, and 9 cases were positive and 4 cases were negative. Among the initial negative cases, 54 children with poor therapeutic effect and high clinical suspicion of inherited metabolic diseases completed genetic testing, 15 cases were positive and 39 cases were negative. The results of the two detection methods were compared, the positive rate of mass spectrometry was 19.4%(13/67), and the positive rate of genetic testing was 35.8%(24/67). The continuity correction of Pearson's chi-square test of continuity correction suggested that the results of genetic testing and mass spectrometry were different, and the difference was statistically significant (P<0.05). Taking genetic testing as the gold standard, the sensitivity and specificity of mass spectrometry detection were 37.5% (95%CI:19.6%-59.2%) and 90.7% (95%CI:76.9%-97.0%), respectively. Among the 24 confirmed cases, 5 cases were diagnosed by gene panel and 19 cases were diagnosed by whole exome sequencing (WES). One case diagnosed by WES had no pathogenic mutation detected by gene panel before diagnosis. The detection of DNM1L gene c.1040C>G and AMN gene c.651+1G>C are novel pathogenic gene variants, which have clinical significance. Conclusions　The ability of mass spectrometry in the diagnosis of inherited metabolic diseases is limited. Genetic testing, especially whole exome sequencing, can be the first choice for individualized diagnosis of suspected rare inherited metabolic diseases. In addition, the new mutation sites found by WES in this study enriched the pathogenic gene mutation spectrum and provided direction for further functional biological experiments.

Novel directions of precision oncology: circulating microbial DNA emerging in cancer-microbiome areas.

Microbiome research has extended into the cancer area in the past decades. Microbes can affect oncogenesis, progression, and treatment response through various mechanisms, including direct regulation and indirect impacts. Microbiota-associated detection methods and agents have been developed to facilitate cancer diagnosis and therapy. Additionally, the cancer microbiome has recently been redefined. The identification of intra-tumoral microbes and cancer-related circulating microbial DNA (cmDNA) has promoted novel research in the cancer-microbiome area. In this review, we define the human system of commensal microbes and the cancer microbiome from a brand-new perspective and emphasize the potential value of cmDNA as a promising biomarker in cancer liquid biopsy. We outline all existing studies on the relationship between cmDNA and cancer and the outlook for potential preclinical and clinical applications of cmDNA in cancer precision medicine, as well as critical problems to be overcome in this burgeoning field.

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.

The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).

A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD.

The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic resonance imaging (fMRI) has emerged as a common neuroimaging technique for studying the brain functional connectome. Most existing methods that have either ignored or simply utilized graph structure, do not fully leverage the potentially important topological information which may be useful in characterizing brain disorders. There is a crucial need for designing novel and efficient approaches which can capture such information. To this end, we propose a new dynamic graph convolutional network (dGCN), which is trained with sparse brain regional connections from dynamically calculated graph features. We also develop a novel convolutional readout layer to improve graph representation. Our extensive experimental analysis demonstrates significantly improved performance of dGCN for ADHD diagnosis compared with existing machine learning and deep learning methods. Visualizations of the salient regions of interest (ROIs) and connectivity based on informative features learned by our model show that the identified functional abnormalities mainly involve brain regions in temporal pole, gyrus rectus, and cerebellar gyri from temporal lobe, frontal lobe, and cerebellum, respectively. A positive correlation was further observed between the identified connectomic abnormalities and ADHD symptom severity. The proposed dGCN model shows great promise in providing a functional network-based precision diagnosis of ADHD and is also broadly applicable to brain connectome-based study of mental disorders.

Study on the Value of DCE-MRI in Differentiating Glossitis and Tongue Cancer and the Intratumour Heterogeneity.

OBJECTIVE: DCE-MRI is an imaging technique that reflects the blood perfusion status of the tissue's microcirculation. The purpose of this article is to explore the clinical value of dynamic contrast-enhanced (DCE)-MRI in distinguishing benign and malignant tongue lesions and the internal heterogeneity of a tumour. METHODS: The patients were divided into a tongue cancer group (22 patients) and a glossitis group (7 patients) based on the pathology results. All of the patients underwent DCE-MRI examination. RESULTS: The results of this study showed that the volume transfer constant (Ktrans), rate constant (Kep), contrast enhancement ratio (CER) and initial area under the gadolinium contrast agent concentration time curve (IAUGG) values of the tongue cancer group were significantly higher than those of the glossitis group, and the difference was statistically significant (P < 0.05). However, the extravascular extracellular volume fraction (Ve), fractional plasma volume (fPV), maximum slope (MaxSlope), and bolus arrival time (BAT) values measured by DCE-MRI in the tongue cancer group were not significantly different from those in the glossitis group (P > 0.05). The results of this study showed that the Ktrans, Kep, and IAUGG values measured by DCE-MRI had a good ability to distinguish tongue inflammation from tumours and Ktrans threshold of 0.484 has the best discriminative ability among them. The mean Ktrans values of stage I-II lesions were significantly higher than that of stage III-IV lesion (p = 0.045). CONCLUSION: DCE-MRI is effective in distinguishing between benign and malignant tongue lesions and the internal heterogeneity of the tumour; it is worth following up in a larger study. CLINICAL REGISTRATION NUMBER: Research registry 6393.

iRGD Peptide-Mediated Liposomal Nanoparticles with Photoacoustic/Ultrasound Dual-Modality Imaging for Precision Theranostics Against Hepatocellular Carcinoma.

PURPOSE: Prepare a multifunctional ultrasound molecular probe, cell-penetrating peptide-modified 10-hydroxycamptothecin-loaded phase-transformation lipid nanoparticles (iRGD-ICG-10-HCPT-PFP-NPs), and to combine iRGD-ICG-10-HCPT-PFP -NPs with low-intensity focused ultrasound (LIFU) for precision theranostics against hepatocellular carcinoma (HCC). MATERIALS AND METHODS: The morphology of nanoparticles (NPs) and iRGD-ICG-10-HCPT-PFP-NPs was detected. In vitro, we examined targeting ability by flow cytometry and confocal laser scanning microscopy (CLSM), assessed penetration ability into hepatoma cells, and assessed killing ability. In vivo, we examined the targeting ability of the NPs with a photoacoustic (PA) imager and fluorometer (FL), while LIFU irradiation was used to trigger the release of chemotherapeutic drugs, which had a therapeutic effect on tumors. RESULTS: The particle size of iRGD-ICG-10-HCPT-PFP-NPs was 298.4 ± 10.42 nm. In vitro, iRGD-ICG-10-HCPT-PFP-NPs bound more to SK-Hep1 cells than ICG-10-HCPT-PFP-NPs. iRGD-ICG-10-HCPT-PFP-NPs could achieve PA/ultrasound imaging. The percentage of antiproliferative and apoptotic cells in the iRGD-ICG-10-HCPT-PFP-NPs+LIFU group was significantly higher. In vivo, iRGD-ICG-10-HCPT-PFP-NPs can target tumor sites and achieve PA/ultrasound imaging. The tumor volume in the iRGD-ICG-10-HCPT-PFP-NPs+LIFU group was significantly smaller, and the antiproliferative and proapoptotic effects were higher. CONCLUSION: We successfully prepared a novel molecular probe that has good targeting, can perform ultrasound/PA dual-modality imaging, and can penetrate deep into tumors to achieve better therapeutic tumor effects, providing a new idea and method for theranostics of HCC.