Differentiating small (< 2 cm) pancreatic ductal adenocarcinoma from neuroendocrine tumors with multiparametric MRI-based radiomic features.

OBJECTIVES: To assess MR-based radiomic analysis in preoperatively discriminating small (< 2 cm) pancreatic ductal adenocarcinomas (PDACs) from neuroendocrine tumors (PNETs). METHODS: A total of 197 patients (146 in the training cohort, 51 in the validation cohort) from two centers were retrospectively collected. A total of 7338 radiomics features were extracted from T2-weighted, diffusion-weighted, T1-weighted, arterial phase, portal venous phase and delayed phase imaging. The optimal features were selected by the Mann-Whitney U test, Spearman's rank correlation test and least absolute shrinkage and selection operator method and used to construct the radiomic score (Rad-score). Conventional radiological and clinical features were also assessed. Multivariable logistic regression was used to construct a radiological model, a radiomic model and a fusion model. RESULTS: Nine optimal features were identified and used to build the Rad-score. The radiomic model based on the Rad-score achieved satisfactory results with AUCs of 0.905 and 0.930, sensitivities of 0.780 and 0.800, specificities of 0.906 and 0.952 and accuracies of 0.836 and 0.863 for the training and validation cohorts, respectively. The fusion model, incorporating CA19-9, tumor margins, pancreatic duct dilatation and the Rad-score, exhibited the best performance with AUCs of 0.977 and 0.941, sensitivities of 0.914 and 0.852, specificities of 0.954 and 0.950, and accuracies of 0.932 and 0.894 for the training and validation cohorts, respectively. CONCLUSIONS: The MR-based Rad-score is a novel image biomarker for discriminating small PDACs from PNETs. A fusion model combining radiomic, radiological and clinical features performed very well in differentially diagnosing these two tumors. CLINICAL RELEVANCE STATEMENT: A fusion model combining MR-based radiomic, radiological, and clinical features could help differentiate between small pancreatic ductal adenocarcinomas and pancreatic neuroendocrine tumors. KEY POINTS: Preoperatively differentiating small pancreatic ductal adenocarcinomas (PDACs) and pancreatic neuroendocrine tumors (PNETs) is challenging. Multiparametric MRI-based Rad-score can be used for discriminating small PDACs from PNETs. A fusion model incorporating radiomic, radiological, and clinical features differentiated small PDACs from PNETs well.

A retrospective study comparing the efficacy of microwave ablation and stereotactic body radiotherapy in colorectal cancer lung metastases.

The purpose of the present study was to assess and compare the efficacy of microwave ablation (MWA) and stereotactic body radiotherapy (SBRT) in the treatment of lung metastases from patients with colorectal cancer (CRC) and to identify the preferable treatment modality based on patient and tumor characteristics. Records of 118 patients with CRC with a total of 307 lung metastases who underwent SBRT or MWA between January 2015 and December 2022 were retrospectively analyzed, including the essential clinicopathological information on patients (age, sex and underlying diseases), diagnosis and treatment information [primary tumor site, levels of carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9], imaging data [diameter of lung metastasis, location of the metastasis (i.e., whether or not the tumor was adjacent to the vessel or bronchus) and internal features] and follow-up data (postoperative therapy, complications or adverse effects and survival outcomes). For statistical analysis of the local tumor progression (LTP), disease-free survival and overall survival (OS) rates, Cox regression analysis, along with the Kaplan-Meier method adjusted using inverse probability of treatment weighting (IPTW), were performed. The median follow-up duration in the present study was 31.5 months. Multivariable Cox regression analysis revealed that the CEA level, metastasis diameter and internal features were independent predictors of OS. In the IPTW-adjusted analysis, no significant difference in the 1-year OS rate was observed between the SBRT and MWA groups (92.9 vs. 93.9%; P=0.483); however, a notable discrepancy in the treatment modalities was noted, leading to significant differences in the 2- and 3-year OS rates (65.9 vs. 57.6%, P=0.001, and 44.7 vs. 36.4%, P<0.001, respectively). A significant interaction effect for the treatment modality was observed for LTP (P=0.021). In conclusion, the present study revealed that SBRT and MWA have similar therapeutic effects in terms of prolonging the survival of patients with CRC with lung metastases; however, regarding the local control of lung metastases, MWA is associated with a number of significant advantages.

Oral administration of RDP58 ameliorated DSS-induced colitis in intestinal microbiota dependent manner.

BACKGROUND: Although the pathogenesis of inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn's disease (CD), has not been fully elucidated, accumulating researches suggest that intestinal microbiota imbalance contributes to the development of IBD in patients and animal models. RDP58, a peptide-based computer-assisted rational design, has been demonstrated to be effective in protecting against a wide range of autoimmune and inflammatory diseases. However, the underlying mechanism by which RDP58 protects against IBD mediated by intestinal microbiota has yet to be elucidated. METHODS: The colitis model was induced by continuously administering 2.5 % (wt/vol) dextran sodium sulfate (DSS) solution for 7 days. The manifestations of colon inflammation were assessed via daily weight changes, colon length, tumor necrosis factor-alpha (TNF-α) level, disease activity index (DAI) score, pathology score, and intestinal barrier permeability. Intestinal microbiota analysis was carried out by 16S-rRNA sequencing. Colonic short chain fatty acids (SCFAs) and regulatory T cells (Tregs) were also detected. To further confirm the protective effect of RDP58 on intestinal microbiota, broad-spectrum antibiotic cocktail (ABX) treatment and fecal microbial transplantation (FMT) experiment were performed. RESULTS: Oral administration of RDP58 ameliorated DSS-induced mice colitis by altering the diversity and composition of intestinal microbiota. Notably, RDP58 significantly upregulated SCFAs-producing microbiota, thereby promoting the generation of Tregs. ABX and FMT were performed to verify the above mechanism. CONCLUSIONS: RDP58 ameliorated DSS-induced colitis through altering intestinal microbiota and enhancing SCFAs and Tregs production in intestinal microbiota dependent manner, potentially provide a novel therapy for IBD.

A multicenter clinical AI system study for detection and diagnosis of focal liver lesions.

Early and accurate diagnosis of focal liver lesions is crucial for effective treatment and prognosis. We developed and validated a fully automated diagnostic system named Liver Artificial Intelligence Diagnosis System (LiAIDS) based on a diverse sample of 12,610 patients from 18 hospitals, both retrospectively and prospectively. In this study, LiAIDS achieved an F1-score of 0.940 for benign and 0.692 for malignant lesions, outperforming junior radiologists (benign: 0.830-0.890, malignant: 0.230-0.360) and being on par with senior radiologists (benign: 0.920-0.950, malignant: 0.550-0.650). Furthermore, with the assistance of LiAIDS, the diagnostic accuracy of all radiologists improved. For benign and malignant lesions, junior radiologists' F1-scores improved to 0.936-0.946 and 0.667-0.680 respectively, while seniors improved to 0.950-0.961 and 0.679-0.753. Additionally, in a triage study of 13,192 consecutive patients, LiAIDS automatically classified 76.46% of patients as low risk with a high NPV of 99.0%. The evidence suggests that LiAIDS can serve as a routine diagnostic tool and enhance the diagnostic capabilities of radiologists for liver lesions.

A nomogram based on CT intratumoral and peritumoral radiomics features preoperatively predicts poorly differentiated invasive pulmonary adenocarcinoma manifesting as subsolid or solid lesions: a double-center study.

Background: The novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models' generalization ability. Methods: We retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance. Results: The AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05). Conclusion: The nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively.

Risk assessment of pneumothorax in colorectal lung metastases treated by percutaneous thermal ablation: a multicenter retrospective cohort study.

PURPOSE: To evaluate the risk of pneumothorax in the percutaneous image-guided thermal ablation (IGTA) treatment of colorectal lung metastases (CRLM). METHODS: Data regarding patients with CRLM treated with IGTA from five medical institutions in China from 2016 to 2023 were reviewed retrospectively. Pneumothorax and non-pneumothorax were compared using the Student's t -test, χ 2 test and Fisher's exact test. Univariate logistic regression analysis was conducted to identify potential risk factors, followed by multivariate logistic regression analysis to evaluate the predictors of pneumothorax. Interactions between variables were examined and used for model construction. Receiver operating characteristic curves and nomograms were generated to assess the performance of the model. RESULTS: A total of 254 patients with 376 CRLM underwent 299 ablation sessions. The incidence of pneumothorax was 45.5%. The adjusted multivariate logistic regression model, incorporating interaction terms, revealed that tumour number [odds ratio (OR)=8.34 (95% CI: 1.37-50.64)], puncture depth [OR=0.53 (95% CI: 0.31-0.91)], pre-procedure radiotherapy [OR=3.66 (95% CI: 1.17-11.40)], peribronchial tumour [OR=2.32 (95% CI: 1.04-5.15)], and emphysema [OR=56.83 (95% CI: 8.42-383.57)] were significant predictive factors of pneumothorax (all P <0.05). The generated nomogram model demonstrated a significant prediction performance, with an area under the receiver operating characteristic curve of 0.800 (95% CI: 0.751-0.850). CONCLUSIONS: Pre-procedure radiotherapy, tumour number, peribronchial tumour, and emphysema were identified as risk factors for pneumothorax in the treatment of CRLM using percutaneous IGTA. Puncture depth was found to be a protective factor against pneumothorax.

Nanotechnology-Based Drug Delivery Systems to Control Bacterial-Biofilm-Associated Lung Infections.

Airway mucus dysfunction and impaired immunological defenses are hallmarks of several lung diseases, including asthma, cystic fibrosis, and chronic obstructive pulmonary diseases, and are mostly causative factors in bacterial-biofilm-associated respiratory tract infections. Bacteria residing within the biofilm architecture pose a complex challenge in clinical settings due to their increased tolerance to currently available antibiotics and host immune responses, resulting in chronic infections with high recalcitrance and high rates of morbidity and mortality. To address these unmet clinical needs, potential anti-biofilm therapeutic strategies are being developed to effectively control bacterial biofilm. This review focuses on recent advances in the development and application of nanoparticulate drug delivery systems for the treatment of biofilm-associated respiratory tract infections, especially addressing the respiratory barriers of concern for biofilm accessibility and the various types of nanoparticles used to combat biofilms. Understanding the obstacles facing pulmonary drug delivery to bacterial biofilms and nanoparticle-based approaches to combatting biofilm may encourage researchers to explore promising treatment modalities for bacterial-biofilm-associated chronic lung infections.

[Construction of Lung Adenocarcinoma Prognosis Model and Drug Sensitivity Analysis Based on Cuproptosis Related Genes].

BACKGROUND: Lung cancer is one of the most common malignant tumors in the world, and the current lung cancer screening and treatment strategies are constantly improving, but its 5-year survival rate is still very low, which seriously endangers human health. Therefore, it is critical to explore new biomarkers to provide personalized treatment and improve the prognosis. Cuproptosis is a newly discovered type of cell death, which is due to the accumulation of excess copper ions in the cell, eventually leading to cell death, which has been suggested by studies to be closely related to the occurrence and development of lung adenocarcinoma (LUAD). Based on The Cancer Genome Atlas (TCGA) database, this study explored the association between cuproptosis-related genes (CRGs) and LUAD prognosis, established a prognostic risk model, and analyzed the interaction between CRGs and LUAD immune cell infiltration. METHODS: The RNA-seq data of LUAD tissue and paracancerous or normal lung tissue were downloaded from the TCGA database; the RNA-seq data of normal lung tissue was downloaded from the Genotype-tissue Expression (GTEx) database, and the data of 462 lung adenocarcinoma cases were downloaded from the Gene Expression Omnibus repository (GEO) as verification. T the risk score model to assess prognosis was constructed by univariate Cox and Lasso-Cox regression analysis, and the predictive ability of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve. Immune-related and drug susceptibility analysis was further performed on high- and low-risk groups. RESULTS: A total of 1656 CRGs and 1356 differentially expressed CRGs were obtained, and 13 CRGs were screened out based on univariate Cox and Lasso-Cox regression analysis to construct a prognostic risk model, and the area under the curves (AUCs) of ROC curves 1-, 3- and 5- year were 0.749, 0.740 and 0.689, respectively. Further study of immune-related functions and immune checkpoint differential analysis between high- and low-risk groups was done. High-risk groups were more sensitive to drugs such as Savolitinib, Palbociclib, and Cytarabine and were more likely to benefit from immunotherapy. CONCLUSIONS: The risk model constructed based on 13 CRGs has good prognostic value, which can assist LUAD patients in individualized treatment, and provides an important theoretical basis for the treatment and prognosis of LUAD.

Discrimination of ground-glass nodular lung adenocarcinoma pathological subtypes via transfer learning: A multicenter study.

BACKGROUND: The surgical approach and prognosis for invasive adenocarcinoma (IAC) and minimally invasive adenocarcinoma (MIA) of the lung differ. However, they both manifest as identical ground-glass nodules (GGNs) in computed tomography images, and no effective method exists to discriminate them. METHODS: We developed and validated a three-dimensional (3D) deep transfer learning model to discriminate IAC from MIA based on CT images of GGNs. This model uses a 3D medical image pre-training model (MedicalNet) and a fusion model to build a classification network. Transfer learning was utilized for end-to-end predictive modeling of the cohort data of the first center, and the cohort data of the other two centers were used as independent external validation data. This study included 999 lung GGN images of 921 patients pathologically diagnosed with IAC or MIA at three cohort centers. RESULTS: The predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). The model had high diagnostic efficacy for the training and validation groups (accuracy: 89%, sensitivity: 95%, specificity: 84%, and AUC: 95% in the training group; accuracy: 88%, sensitivity: 84%, specificity: 93%, and AUC: 92% in the internal validation group; accuracy: 83%, sensitivity: 83%, specificity: 83%, and AUC: 89% in one external validation group; accuracy: 78%, sensitivity: 80%, specificity: 77%, and AUC: 82% in the other external validation group). CONCLUSIONS: Our 3D deep transfer learning model provides a noninvasive, low-cost, rapid, and reproducible method for preoperative prediction of IAC and MIA in lung cancer patients with GGNs. It can help clinicians to choose the optimal surgical strategy and improve the prognosis of patients.

A convenient scoring system to distinguish intrahepatic mass-forming cholangiocarcinoma from solitary colorectal liver metastasis based on magnetic resonance imaging features.

OBJECTIVES: To develop and validate a diagnostic scoring system to differentiate intrahepatic mass-forming cholangiocarcinoma (IMCC) from solitary colorectal liver metastasis (CRLM). METHODS: A total of 366 patients (263 in the training cohort, 103 in the validation cohort) who underwent MRI examination with pathologically proven either IMCC or CRLM from two centers were included. Twenty-eight MRI features were collected. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors for distinguishing IMCC from solitary CRLM. The independent predictors were weighted over based on regression coefficients to build a scoring system. The overall score distribution was divided into three groups to show the diagnostic probability of CRLM. RESULTS: Six independent predictors, including hepatic capsular retraction, peripheral hepatic enhancement, vessel penetrating the tumor, upper abdominal lymphadenopathy, peripheral washout at the portal venous phase, and rim enhancement at the portal venous phase were included in the system. All predictors were assigned 1 point. At a cutoff of 3 points, AUCs for this score model were 0.948 and 0.903 with sensitivities of 96.5% and 92.0%, specificities of 84.4% and 71.7%, positive predictive values of 87.7% and 75.4%, negative predictive values of 95.4% and 90.5%, and accuracies of 90.9% and 81.6% for the training and validation cohorts, respectively. An increasing trend was shown in the diagnostic probability of CRLM among the three groups based on the score. CONCLUSIONS: The established scoring system is reliable and convenient for distinguishing IMCC from solitary CRLM using six MRI features. CLINICAL RELEVANCE STATEMENT: A reliable and convenient scoring system was developed to differentiate between intrahepatic mass-forming cholangiocarcinoma from solitary colorectal liver metastasis using six MRI features. KEY POINTS: • Characteristic MRI features were identified to distinguish intrahepatic mass-forming cholangiocarcinoma (IMCC) from solitary colorectal liver metastasis (CRLM). • A model to distinguish IMCC from solitary CRLM was created based on 6 features, including hepatic capsular retraction, upper abdominal lymphadenopathy, peripheral washout at the portal venous phase, rim enhancement at the portal venous phase, peripheral hepatic enhancement, and vessel penetrating the tumor.

Elimination of Carbides in Carburized Layer of Stainless Steel/Carbon Steel by Horizontal Continuous Liquid-Solid Composite Casting.

The carbides in the carburized layer of stainless steel (SS)/carbon steel (CS) clad plates are prone to inducing intergranular cracks and reducing the interfacial bonding strength. In this paper, SS/CS clad plates were fabricated by horizontal continuous liquid-solid composite casting (HCLSCC), and the formation mechanism of the interfacial carbides and their effect on the elimination of carbides in the carburized layer were revealed by numerical simulation and thermodynamic calculations. During the HCLSCC process, the cladding interface encountered re-melting and re-solidification after rapid melting and solidification, resulting in liquid-liquid and solid-solid diffusion at the cladding interface, where the atomic ratio of Cr/C (Cr/C) gradually increased. Therefore, strip M7C3 and M23C6 carbides as well as blocky M23C6 carbides formed at the cladding interface in turn and had a coherent relationship with the matrix. The blocky M23C6 carbides led to an increase of 240% in the interfacial ferrite strength. The formation of interfacial carbides reduced the difference in C activity between the cladding interface and SS, thus preventing the diffusion of C to SS and inhibiting carbide precipitation in the carburized layer of SS, which was beneficial to improving the interfacial bonding strength.

Fe3+@PDOPA­b­PSar Nanoparticles for Magnetic Resonance Imaging and Cancer Chemotherapy.

Purpose: Chemotherapy treatments for cancer are always accompanied by a low concentration of drug delivered in the tumor area and severe side effects including systemic toxicity. Improving the concentration, biocompatibility, and biodegradability of regional chemotherapy drugs is a pressing challenge in the field of materials. Methods: N-Phenyloxycarbonyl-amino acids (NPCs) which exhibit significant tolerance to nucleophiles, such as water and hydroxyl-containing compounds, are promising monomers for the synthesis of polypeptides and polypeptoids. Cell line and mouse models were used to comprehensively explore how to enhance the tumor MRI signal and evaluate the therapeutic effect of Fe@POS-DOX nanoparticles. Results: In this study, poly(3,4-dihydroxy-L-phenylalanine)-b-polysarcosine (PDOPA-b-PSar, simplified as POS) was synthesized by the block copolymerization of DOPA-NPC with Sar-NPC. Fe@POS-DOX nanoparticles were prepared in order to utilize the strong chelation of catechol ligands to iron (III) cations and the hydrophobic interaction between DOX and DOPA block to deliver chemotherapeutics to tumor tissue. The Fe@POS-DOX nanoparticles exhibit high longitudinal relaxivity (r 1 = 7.06 mM-1·s-1) and act as T 1-weighted magnetic resonance (MR) imaging contrast agents. Further, the main focus was improving tumor site-specific bioavailability and achieving therapeutic effects through the biocompatibility and biodegradability of Fe@POS-DOX NPs. The Fe@POS-DOX treatment exhibited excellent antitumor effects. Conclusion: Upon intravenous injection, Fe@POS-DOX delivers DOX specifically to the tumor tissues, as revealed by MR, and leads to the inhibition of tumor growth without overt toxicity to normal tissues, thus displaying considerable potential for use in clinical applications.

Preoperative prediction of tumor deposits in rectal cancer with clinical-magnetic resonance deep learning-based radiomic models.

Background: This study aimed to establish an effective model for preoperative prediction of tumor deposits (TDs) in patients with rectal cancer (RC). Methods: In 500 patients, radiomic features were extracted from magnetic resonance imaging (MRI) using modalities such as high-resolution T2-weighted (HRT2) imaging and diffusion-weighted imaging (DWI). Machine learning (ML)-based and deep learning (DL)-based radiomic models were developed and integrated with clinical characteristics for TD prediction. The performance of the models was assessed using the area under the curve (AUC) over five-fold cross-validation. Results: A total of 564 radiomic features that quantified the intensity, shape, orientation, and texture of the tumor were extracted for each patient. The HRT2-ML, DWI-ML, Merged-ML, HRT2-DL, DWI-DL, and Merged-DL models demonstrated AUCs of 0.62 ± 0.02, 0.64 ± 0.08, 0.69 ± 0.04, 0.57 ± 0.06, 0.68 ± 0.03, and 0.59 ± 0.04, respectively. The clinical-ML, clinical-HRT2-ML, clinical-DWI-ML, clinical-Merged-ML, clinical-DL, clinical-HRT2-DL, clinical-DWI-DL, and clinical-Merged-DL models demonstrated AUCs of 0.81 ± 0.06, 0.79 ± 0.02, 0.81 ± 0.02, 0.83 ± 0.01, 0.81 ± 0.04, 0.83 ± 0.04, 0.90 ± 0.04, and 0.83 ± 0.05, respectively. The clinical-DWI-DL model achieved the best predictive performance (accuracy 0.84 ± 0.05, sensitivity 0.94 ± 0. 13, specificity 0.79 ± 0.04). Conclusions: A comprehensive model combining MRI radiomic features and clinical characteristics achieved promising performance in TD prediction for RC patients. This approach has the potential to assist clinicians in preoperative stage evaluation and personalized treatment of RC patients.

Integrated manganese (III)-doped nanosystem for optimizing photothermal ablation: Amplifying hyperthermia-induced STING pathway and enhancing antitumor immunity.

Despite the great promise initially demonstrated by photothermal ablation (PTA) therapy, its inability to completely ablate large tumors is problematic, because this has been found to result in residual tumors at ablation margins and bring a relative high rate of subsequent recurrences and metastases. To address this issue, we herein report a smart photothermal nanosystem (PBM) based on FDA-approved Prussian blue (PB) nanoparticles, doped with Mn (III) to suppress the tumor debris left by incomplete ablation. Notably, our study demonstrated that PTA-induced hyperthermia plays a crucial role in initiating the cGAS-STING pathway by generating damaged cytosolic DNA. This PBM nanosystem, which consumes glutathione and continuously releases Mn(II), further amplifies the PTA-induced cGAS-STING pathway in CT26 colon and 4T1 breast tumor models. Moreover, treatment with PBM following PTA boosted the robust immune response in situ and extended to the whole body with a remarkable suppression effect on both local residual and distant tumors. This work, which improves the antitumor efficacy of nonablated areas utilizing hyperthermia-enhanced immune therapy, may therefore provide a promising adjuvant antitumor strategy for the issue of incomplete ablation. STATEMENT OF SIGNIFICANCE: This work discovered, for the first time, that photothermal ablation-induced hyperthermia plays a crucial role in initiating the cGAS-STING pathway. Taking advantage of this finding, we developed a smart photothermal material (PBM) tailored for incomplete tumor ablation. This integrated Mn(III)-doped nanosystem (PBM) demonstrated superior therapeutic benefits due to the thermal ablation process and immune enhancement. As the photothermal ablation-induced cGAS-STING pathway was triggered, the released Mn(III) consumes GSH while continuously transferred to Mn(II), which further amplified STING activation and facilitated a more robust antitumor immunity, thereby remarkably inhibiting both local residual and distant tumors in virtue of the biological changes under thermal ablation.

Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer.

OBJECTIVES: To investigate the feasibility and efficacy of a deep-learning (DL)-based three-dimensional (3D) super-resolution (SR) MRI radiomics model for preoperative T-staging prediction in rectal cancer (RC). METHODS: Seven hundred six eligible RC patients (T1/2 = 287, T3/4 = 419) were retrospectively enrolled in this study and chronologically allocated into a training cohort (n = 565) and a validation cohort (n = 141). We conducted a deep-transfer-learning network on high-resolution (HR) T2-weighted imaging (T2WI) to enhance the z-resolution of the images and acquired the preoperative SRT2WI. The radiomics models named modelHRT2 and modelSRT2 were respectively constructed with high-dimensional quantitative features extracted from manually segmented volume of interests of HRT2WI and SRT2WI through the Least Absolute Shrinkage and Selection Operator method. The performances of the models were evaluated by ROC, calibration, and decision curves. RESULTS: ModelSRT2 outperformed modelHRT2 (AUC 0.869, sensitivity 71.1%, specificity 93.1%, and accuracy 83.3% vs. AUC 0.810, sensitivity 89.5%, specificity 70.1%, and accuracy 77.3%) in distinguishing T1/2 and T3/4 RC with significant difference (p < 0.05). Both radiomics models achieved higher AUCs than the expert radiologists (0.685, 95% confidence interval 0.595-0.775, p < 0.05). The calibration curves confirmed high goodness of fit, and the decision curve analysis revealed the clinical value. CONCLUSIONS: ModelSRT2 yielded superior predictive performance in preoperative RC T-staging by comparison with modelHRT2 and expert radiologists' visual assessments. KEY POINTS: • For the first time, DL-based 3D SR images were applied in radiomics analysis for clinical utility. • Compared with the visual assessment of expert radiologists and the conventional radiomics model based on HRT2WI, the SR radiomics model showed a more favorable capability in helping clinicians assess the invasion depth of RC preoperatively. • This is the largest radiomics study for T-staging prediction in RC.

Melanotic medullary thyroid carcinoma: A case report with review of the literature.

Melanotic medullary thyroid carcinoma is morphologically defined by the presence of melanin deposits in the cytoplasm of tumor cells. It is an extremely rare variant with only 15 cases described in the literature to date and only one report of diagnosis by fine needle aspiration (FNA) biopsy. A 51-year-old woman presented with neck swelling. An ultrasound examination revealed a single solid nodule in the right thyroid lobe that measured 5.4 × 4.7 × 4.3 cm. Laboratory examination revealed elevated levels of serum calcitonin (8643.0 pg/ml), carcinoembryonic antigen (CEA) (86.2 ng/ml), and chromogranin A (123.2 ng/ml). An FNA biopsy of the thyroid nodule revealed predominantly single plasmacytoid cells with round to oval eccentric nuclei and dark brown intracytoplasmic granules. Immunohistochemical studies with Melan-A performed on a cell block slide confirmed that the granules contained melanin. The tumor cells were also positive for calcitonin, CEA, synaptophysin, AE1/AE3, CAM5.2, and HMB-45(focal); the tumor cells were negative for chromogranin, thyroglobulin, PAX8 and TTF-1. The diagnosis was reported as melanotic variant of medullary thyroid carcinoma. The patient underwent a total thyroidectomy which revealed tumor cell expression of insulinoma-associated protein 1 and confirmed neuroendocrine differentiation. Shortly after she presented with tumor recurrence in the thyroidectomy bed. The tumor cells were positive for only S100, SOX10, and Melan-A. Molecular analysis with the SEMA4 Solid Tumor Panel revealed mutations in the HRAS, PIK3CA, PIK3R1, MYC, and CCND3 genes. The final diagnosis was reported as melanocytic medullary thyroid carcinoma with high grade transformation and loss of epithelial and neuroendocrine expression.

Diagnostic Value and Prognostic Significance of Procalcitonin Combined with C-Reactive Protein in Patients with Bacterial Bloodstream Infection.

Objective: To study the clinical values and implications for the prognosis of procalcitonin (PCT) combined with C-reactive protein (hs-CRP) in patients with bacterial bloodstream infection. Methods: One hundred and twenty patients with infection hospitalized from Mar. 2020 to Jun. 2021 were chosen as subjects. All participants were tested for serum PCT, hs-CRP, and blood culture. According to the types of pathogenic bacteria, they were divided into the gram-negative bacteria bloodstream infection group (n = 53) and the gram-positive bacteria bloodstream infection group (n = 31). Depending on the prognostic outcome of the participants after 28 days, they were categorized into survival and fatality cohorts. The PCT and hs-CRP levels were compared to explore diagnostic value implications for the prognosis of the cases with bacterial bloodstream infection. Results: Serum PCT and hs-CRP values in the positive cohort were higher than those in the negative cohort. The levels of serum PCT and hs-CRP in pulmonary infection were higher than those in the group with negative cases, and the difference was statistically significant (P < 0.05). There were 27 gram-positive participants and 9 gram-negative cases in the positive cohort. The serum PCT value of gram-negative bacterial infection was greater than that of gram-positive bacterial infection. The value of serum PCT in the gram-negative bacterial infection group was higher than that in the gram-positive bacterial infection group, and the difference was statistically significant (P < 0.05). The areas under the curve (AUCs) of PCT, combination of hs-CRP and PCT, and hs-CRP were 0.946, 0.783, and 0.991, respectively. The combined examination of PCT and hs-CRP was the largest, PCT was the second, and hs-CRP was the lowest. These results indicated that the accuracy of combined detection of PCT and hs-CRP in the diagnostic bloodstream infection was the highest (0.991), followed by PCT (0.946) and the lowest (0.783). The PCT and hs-CRP levels of the survival cohort were lower than those in the death cohort. AUCs of PCT, hs-CRP and PCT, and hs-CRP were 0.848, 0.826, and 0.934, respectively. The combined examination of PCT and hs-CRP was the largest, followed by PCT and hs-CRP. The accuracy of the combination of PCT and hs-CRP was the highest (0.934), followed by PCT (0.848), and the diagnostic accuracy of hs-CRP was the lowest (0.826). Conclusion: There were significant differences in the levels of PCT and CRP between the gram-positive bacteria group and the gram-positive bacteria group. PCT and CRP have high diagnostic values in predicting the short-term prognosis of patients. PCT and CRP assist clinical diagnosis and guide treatment and play a positive role in early treatment and prognosis evaluation of patients.

Incremental Value of Radiomics in 5-Year Overall Survival Prediction for Stage II-III Rectal Cancer.

Although rectal cancer comprises up to one-third of colorectal cancer cases and several prognosis nomograms have been established for colon cancer, statistical tools for predicting long-term survival in rectal cancer are lacking. In addition, previous prognostic studies did not include much imaging findings, qualitatively or quantitatively. Therefore, we include multiparametric MRI information from both radiologists' readings and quantitative radiomics signatures to construct a prognostic model that allows 5-year overall survival (OS) prediction for advance-staged rectal cancer patients. The result suggested that the model combined with quantitative imaging findings might outperform that of conventional TNM staging or other clinical prognostic factors. It was noteworthy that the identified radiomics signature consisted of three from dynamic contrast-enhanced (DCE)-MRI, four from anatomical MRI, and one from functional diffusion-weighted imaging (DWI). This highlighted the importance of multiparametric MRI to address the issue of long-term survival estimation in rectal cancer. Additionally, the constructed radiomics signature demonstrated value to the conventional prognostic factors in predicting 5-year OS for stage II-III rectal cancer. The presented nomogram also provides a practical example of individualized prognosis estimation and may potentially impact treatment strategies.