Synthetic MRI and diffusion-weighted imaging for differentiating nasopharyngeal lymphoma from nasopharyngeal carcinoma: combination with morphological features.

OBJECTIVES: To investigate the feasibility of synthetic MRI (syMRI), diffusion-weighted imaging (DWI) and their combination with morphological features for differentiating nasopharyngeal lymphoma (NPL) from nasopharyngeal carcinoma (NPC). METHODS: Sixty-nine patients with nasopharyngeal tumors (NPL, n = 22; NPC, n = 47) who underwent syMRI and DWI were retrospectively enrolled between October 2020 and May 2022. syMRI and DWI quantitative parameters (T1, T2, PD, ADC), and morphological features were obtained. Diagnostic performance was assessed by independent sample t-test, chi-square test, logistic regression analysis, receiver operating characteristic curve (ROC), and DeLong test. RESULTS: NPL has significantly lower T2, PD, and ADC values compared to NPC (all P < 0.05), whereas no significant difference was found in T1 value between these two entities (P > 0.05). The morphological features of tumor type, skull-base involvement, Waldeyer ring involvement, and lymph nodes involvement region were significantly different between NPL and NPC (all P < 0.05). The syMRI (T2+PD) model has better diagnostic efficacy, with AUC, sensitivity, specificity, and accuracy of 0.875, 77.27%, 89.36%, and 85.51%. Compared with syMRI model, syMRI+Morph (PD+Waldeyer ring involvement+lymph nodes involvement region), syMRI+DWI (T2+PD+ADC), and syMRI+DWI+Morph (PD+ADC+skull base involvement+Waldeyer ring involvement) models can further improve the diagnostic efficiency (all P < 0.05). Furthermore, syMRI+DWI+Morph model has excellent diagnostic performance, with AUC, sensitivity, specificity, and accuracy of 0.986, 95.47%, 97.87%, and 97.10%, respectively. CONCLUSION: syMRI and DWI quantitative parameters were helpful in discriminating NPL from NPC. syMRI+DWI+Morph model has the excellent diagnostic efficiency in differentiating these two entities. ADVANCES IN KNOWLEDGE: syMRI+DWI+morphological feature method can differentiate NPL from NPC with excellent diagnostic performance.

Spectral CT in the evaluation of perineural invasion status in rectal cancer.

PURPOSE: To investigate whether preoperative spectral CT quantitative parameters can assess perineural invasion (PNI) status in rectal cancer. METHODS: Sixty-two patients diagnosed with rectal cancer who underwent preoperative spectral CT were retrospectively enrolled and divided into positive and negative PNI groups according to histopathologic results. The CT attenuation value (HU) of virtual monochromatic images (40-70 keV), spectral curve slope (K(HU)), effective atomic number (Zeff), and iodine concentration (IC) from spectral CT were compared between these two groups using t test or rank sum test. A nomogram was established by incorporating the independent predictors to assess the overall diagnostic efficacy. The area under the ROC curves (AUCs) were compared using the DeLong test. RESULTS: The preoperative spectral CT parameters (40-70 keV attenuation, K(HU), Zeff, and IC) were significantly higher in the PNI-positive group compared to the PNI-negative group (all p < 0.05). The highest predictive efficiency of PNI was observed at 40 keV attenuation, with an area under the curve (AUC), sensitivity, specificity, and accuracy of 0.847, 81.8%, 72.5%, and 75.8%, respectively. Binary logistic regression demonstrated that the clinical feature (cN stage) and 40 keV attenuation were independent predictors of PNI status. The nomogram incorporating these two predictors (cN stage and 40 keV attenuation) exhibited the best evaluation efficacy, with an AUC, sensitivity, specificity, and accuracy of 0.885, 86.4%, 77.5%, and 80.6%. CONCLUSION: Spectral CT quantitative parameters proved valuable in the preoperative assessment of PNI status in rectal cancer patients. The combination of spectral CT parameters and clinical features could further enhance the diagnostic efficiency.

Multiplexed sensitivity-encoding versus single-shot echo-planar imaging: a comparative study for diffusion-weighted imaging of the thyroid lesions.

PURPOSE: To compare multiplexed sensitivity-encoding diffusion-weighted magnetic resonance imaging (MUSE-DWI) and conventional DWI (cDWI) techniques in thyroid MRI. MATERIALS AND METHODS: Nineteen patients who underwent thyroid MRI using both MUSE-DWI and cDWI at a 3.0 T MRI system were enrolled. Qualitative parameters (image quality, thyroid contour, and lesion conspicuity) and quantitative parameters (signal-to-noise ratio (SNR), lesion-to-thyroid contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC)) were compared between the two sequences. In addition, ADC values derived from MUSE-DWI and cDWI were separately compared between benign and malignant lesions. RESULTS: MUSE-DWI outperformed cDWI in terms of image quality, thyroid contour, and lesion conspicuity. Significantly, higher signal-to-noise ratio (SNR) in both the thyroid and its lesion were found in MUSE-DWI than those in cDWI (both P < 0.05). The lesion-to-thyroid contrast-to-noise ratio (CNR) values were also significantly higher in MUSE-DWI than those in cDWI (P < 0.05). The apparent diffusion coefficient (ADC) of the thyroid in MUSE-DWI was significantly lower than that in cDWI (P < 0.05). The ADC of the lesion in MUSE-DWI was also significantly lower than that in cDWI (P < 0.05). In addition, ADC values derived from MUSE-DWI and cDWI were significantly higher in benign lesions than malignant lesions (P < 0.05). CONCLUSION: Compared with cDWI, MUSE-DWI can improve the image quality, thyroid contour sharpness, lesion conspicuity, SNR in both the thyroid and its lesions, and enhancing the CNR between lesions and thyroid.

Synthetic MRI quantitative parameters in discriminating stage T1 nasopharyngeal carcinoma and benign hyperplasia: Combination with morphological features.

PURPOSE: To investigate the feasibility of synthetic MRI (syMRI) quantitative parameters and its combination with morphological features in discriminating stage T1 nasopharyngeal carcinoma (T1-NPC) and benign hyperplasia (BH). MATERIAL AND METHODS: Eighty-eight patients with nasopharyngeal lesions (T1-NPC, n = 54; BH, n = 34) were retrospectively enrolled between October 2020 and May 2022. The syMRI quantitative parameters of nasopharyngeal lesions (T1, T2, PD, T1SD, T2SD, PDSD) and longus capitis (T1, T2, PD) were measured, and T1ratio, T2ratio and PDratio were calculated (lesion/longus capitis). The morphological features (lesion pattern, retention cyst, serrated protrusion, middle ear effusion, tumor volume, and retropharyngeal lymph node) were compared. Statistical analyses were performed using the independent sample t test, Chi-square test, logistic regression analysis, receiver operating characteristic curve (ROC), and DeLong test. RESULTS: The T1, T2, PD, T1SD, T1ratio, and T2ratio values of T1-NPC were significantly lower than those of BH. The morphological features (lesion pattern, retention cyst, retropharyngeal lymph node) were significant difference between these two entities. T2 value has the highest AUC in all syMRI quantitative parameters, followed by T1, T1ratio, PD, T2ratio and T1SD. Combined syMRI quantitative parameters (T2, PD, T1ratio) can further improve the diagnosis efficiency. Combined syMRI parameters and morphological feature (T2, PD, lesion pattern, retropharyngeal lymph node) has the excellent diagnostic efficiency, with AUC, sensitivity, specificity, and accuracy of 0.979, 96.30%, 97.06%, 96.77%. CONCLUSIONS: Synthetic MRI was helpful in distinguishing T1-NPC from BH, and combined syMRI quantitative parameters and morphological features has the optimal diagnostic performance.

Can texture analysis of T2-weighted MRI be used to predict extrathyroidal extension in papillary thyroid carcinoma?

Determining the presence of extrathyroidal extension (ETE) is important for established of different surgical protocol and postoperative patient management in patients with papillary thyroid carcinoma (PTC). The correlation relationship between texture features from T2-weighted imaging (T2WI) and ETE has not been explored extensively. This study aimed to explore the value of T2-weighted magnetic resonance imaging - based whole tumor texture analysis in predict extrathyroidal extension with PTC. In this retrospectively study, 76 patients with pathologically proven PTC were recruited, who received surgical resection and underwent preoperative thyroid magnetic resonance imaging. Based on histo-pathologically findings, patients were classified into ETE and no ETE groups. ETE group was further divided into 2 subgroups (minimal ETE and extensive ETE). Whole-tumor texture analysis was independently performed by 2 radiologists on axial T2WI images. Nine histogram and gray-level co-occurrence matrix (GLCM) texture features were automatically extracted. Univariate and multivariate analysis were performed to determine risk factors associated with ETE. Predictive performance was evaluated by receiver operating characteristic (ROC) analysis. Interobserver agreement, confirmed by intraclass correlation coefficients (ICCs) ranging from 0.78 to 0.89, was excellent for texture analysis between 2 radiologists. T2WI image derived entropy, standard deviation, energy and correlation have significant difference between PTC with and without ETE (all P < .05). Among these, entropy showed the best diagnostic efficiency with the area under ROC curve of 0.837, diagnostic threshold of 5.86, diagnostic sensitivity and specificity of 81.5% and 75.6%, respectively. Additionally, the multivariate analysis revealed that high entropy was an independent risk factor of ETE (odds ratio, OR = 19.348; 95%CI, 4.578-81.760; P = .001). The findings indicate a significant association between texture features of the primary tumor based on T2WI and the presence of ETE in PTC. These results have the potential to help predict ETE preoperatively in patients with PTC, offering valuable insights for clinical decision-making.

A nomogram for predicting postoperative complications based on tumor spectral CT parameters and visceral fat area in gastric cancer patients.

PURPOSE: To construct a nomogram combining tumor spectral CT parameters and visceral fat area (VFA) to predict postoperative complications (POCs) in patients with gastric cancer (GC). METHOD: This retrospective study included 101 GC patients who underwent preoperative abdominal spectral CT scan and were divided into two groups (37 with POCs and 64 without POCs) according to the Clavien-Dindo classification standard. Logistic regression was used to establish spectral, VFA, and combined models for predicting POCs. The combined prediction model was presented as a nomogram, and the diagnostic performance of each model was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: The AUCs of the VFA and spectral model were 0.71 (95% CI: 0.62-0.80) and 0.81 (95% CI: 0.72-0.88), respectively. VFA, the slope of spectral curve (λ) in venous phase (λ-VP) and tumor Hounsfield units on monoenergetic images 40 keV in VP (MonoE40keV-VP) were independent predictors of POCs in GC. The nomogram yielded an AUC of 0.89 (95% CI: 0.81-0.94). The combined model was superior to the VFA or spectral models by comparing their AUCs (P = 0.000 and 0.022). CONCLUSIONS: The nomogram based on two tumor spectral parameters (λ-VP, MonoE40keV-VP) and VFA could serve as a convenient tool for predicting the POCs of GC patients.

Predictive modeling based on tumor spectral CT parameters and clinical features for postoperative complications in patients undergoing colon resection for cancer.

BACKGROUND: Colon cancer is a particularly prevalent malignancy that produces postoperative complications (POCs). However, limited imaging modality exists on the accurate diagnosis of POCs. The purpose of this study was therefore to construct a model combining tumor spectral CT parameters and clinical features to predict POCs before surgery in colon cancer. METHODS: This retrospective study included 85 patients who had preoperative abdominal spectral CT scans and underwent radical colon cancer resection at our institution. The patients were divided into two groups based on the absence (no complication/grade I) or presence (grades II-V) of POCs according to the Clavien-Dindo grading system. The visceral fat areas (VFA) of patients were semi-automatically outlined and calculated on L3-level CT images using ImageJ software. Clinical features and tumor spectral CT parameters were statistically compared between the two groups. A combined model of spectral CT parameters and clinical features was established by stepwise regression to predict POCs in colon cancer. The diagnostic performance of the model was evaluated using the receiver operating characteristic (ROC) curve, including area under the curve (AUC), sensitivity, and specificity. RESULTS: Twenty-seven patients with POCs and 58 patients without POCs were included in this study. MonoE40keV-VP and VFA were independent predictors of POCs. The combined model based on predictors yielded an AUC of 0.84 (95% CI: 0.74-0.91), with a sensitivity of 77.8% and specificity of 87.9%. CONCLUSIONS: The model combining MonoE40keV-VP and VFA can predict POCs before surgery in colon cancer and provide a basis for individualized management plans. CRITICAL RELEVANCE STATEMENT: The model combining MonoE40keV-VP and visceral fat area can predict postoperative complications before surgery in colon cancer and provide a basis for individualized management plans. KEY POINTS: • Visceral fat area and MonoE40keV-VP were independent predictors of postoperative complications in colon cancer. • The combined model yielded a high AUC, sensitivity, and specificity in predicting postoperative complications. • The combined model was superior to the single visceral fat area or MonoE40keV-VP in predicting postoperative complications.

Chiral Iron Oxide Supraparticles Enable Enantiomer-Dependent Tumor-Targeted Magnetic Resonance Imaging.

The chemical, physical and biological effects of chiral nanomaterials have inspired general interest and demonstrated important advantages in fundamental science. Here, chiral iron oxide supraparticles (Fe3 O4 SPs) modified by chiral penicillamine (Pen) molecules with g-factor of ≈2 × 10-3 at 415 nm are fabricated, and these SPs act as high-quality magnetic resonance imaging (MRI) contrast agents. Therein, the transverse relaxation efficiency and T2 -MRI results demonstrated chiral Fe3 O4 SPs have a r2 relaxivity of 157.39 ± 2.34 mM-1 ·S-1 for D-Fe3 O4 SPs and 136.21 ± 1.26 mM-1 ·S-1 for L-Fe3 O4 SPs due to enhanced electronic transition dipole moment for D-Fe3 O4 SPs compared with L-Fe3 O4 SPs. The in vivo MRI results show that D-Fe3 O4 SPs exhibit two-fold lower contrast ratio than L-Fe3 O4 SPs, which enhances targeted enrichment in tumor tissue, such as prostate cancer, melanoma and brain glioma tumors. Notably, it is found that D-Fe3 O4 SPs have 7.7-fold higher affinity for the tumor cell surface receptor cluster-of-differentiation 47 (CD47) than L-Fe3 O4 SPs. These findings uncover that chiral Fe3 O4 SPs act as a highly effective MRI contrast agent for targeting and imaging broad tumors, thus accelerating the practical application of chiral nanomaterials and deepening the understanding of chirality in biological and non-biological environments.

Combination of transcatheter arterial chemoembolization and anti-PD-L1 liposomes therapy suppressed hepatocellular carcinoma progression in mice.

BACKGROUND: Hepatocellular carcinoma (HCC) is a serious life-threatened tumor with high morbidity and mortality. This study aimed to study the effects of combination TACE and anti-PD-L1 liposome drug in treating HCC in mice models. METHODS: We constructed the liposome drug with phosphatidylcholine and cholesterol and mannitol, etc. Besides, the HCC mice model was established through abdominal subcutaneous injection HepG2 cancer cells in mice, then the PE-10 polyethylene catheter was used for TACE therapy. The mice were separately received transcatheter arterial chemoembolization treatment, avelumab liposome drug therapy, and TACE combined with avelumab liposome drug therapy. Flow cytometry was used to analyze cell apoptosis. Western blot, Immunofluorescence staining, real-time PCR were performed to detect protein and gene expressions. RESULTS: The liposomes drug was successfully constructed with a diameter of (125.5 ± 15.3) nm. After the mice received TACE and (or) immunotherapy, the combined liposome drug therapy significantly reduced the volume of hepatic carcinoma tissues, besides, the apoptotic rate of hepatic carcinoma cells in the combined liposome drug treatment group was increased obviously compared with other groups. Moreover, the protein TGFßR2 located in the cellular membrane was obviously down-regulated in the combined liposome drug therapy, while the expression of SMAD7 and PTPN14 was up-regulated in the treatment groups compared with the mice without treatment, besides, the protein PTPN14 was mainly located in the nucleus. Additionally, the mRNA expression of genes SNAI1 and Vimentin was significantly down-regulated in the combined liposome drug therapy. CONCLUSION: Combination of transcatheter arterial chemoembolization and anti-PD-L1 liposome drug therapy significantly suppressed hepatocellular carcinoma proliferation and metastasis in mice models.

Deep learning-extracted CT imaging phenotypes predict response to total resection in colorectal cancer.

BACKGROUND: Deep learning surpasses many traditional methods for many vision tasks, allowing the transformation of hierarchical features into more abstract, high-level features. PURPOSE: To evaluate the prognostic value of preoperative computed tomography (CT) image texture features and deep learning self-learning high-throughput features (SHF) on postoperative overall survival in the treatment of patients with colorectal cancer (CRC). MATERIAL AND METHODS: The dataset consisted of 810 enrolled patients with CRC confirmed from 10 November 2011 to 10 February 2018. In contrast, SHF extracted by deep learning with multi-task training mechanism and texture features were extracted from the CT with tumor volume region of interest, respectively, and combined with the Cox proportional hazard (CoxPH) model for initial validation to obtain a RAD score to classify patients into high- and low-risk groups. The SHF stability was further validated in combination with Neural Multi-Task Logistic Regression (N-MTLR) model. The overall recognition ability and accuracy of CoxPH and N-MTLR model were evaluated by C-index and Integrated Brier Score (IBS). RESULTS: SHF had a more significant degree of differentiation than texture features. The result is (SHF vs. texture features: C-index: 0.884 vs. 0.611; IBS: 0.025 vs. 0.073) in the CoxPH model, and (SHF vs. texture features: C-index: 0.861 vs. 0.630; IBS: 0.024 vs. 0.065) in N-MTLR. CONCLUSION: SHF is superior to texture features and has potential application for the preoperative prediction of the individualized treatment of CRC.

CT and MR imaging features of mixed neuroendocrine-non-neuroendocrine neoplasm of the pancreas compared with pancreatic ductal adenocarcinoma and neuroendocrine tumor.

OBJECTIVE: This study aimed to assess the computed tomography (CT) and magnetic resonance imaging (MRI) features of pancreatic mixed neuroendocrine-non-neuroendocrine neoplasm (MiNEN) and compare them with those of pancreatic ductal adenocarcinoma (PDAC) and neuroendocrine tumor (NET). METHODS: Twelve patients with pancreatic MiNEN, 24 patients with PDAC, and 24 patients with NET, who underwent both contrast-enhanced CT and MRI, were included. Clinical data and the key imaging features were retrospectively evaluated by two independent readers and compared between MiNEN and PDAC or NET. Univariate and multivariable logistic regression analyses were performed to obtain predictors for pancreatic MiNEN. RESULTS: Patients with pancreatic MiNEN more frequently presented with large size and heterogeneous and cystic components compared with PDAC (p < 0.031) and ill-defined irregular margins, progressive enhancement, and adjacent organ involvement compared with NET (p < 0.036). However, vascular invasion was less commonly seen in MiNEN than PDAC (p = 0.010). Moderate enhancement was observed more frequently in MiNEN than in PDAC or NET (p < 0.001). Multivariate logistic analyses demonstrated that moderate enhancement and ill-defined irregular margin were the most valuable features for the prediction of pancreatic MiNEN (p ≤ 0.044). The combination of the two features resulted in a specificity of 93.8%, sensitivity of 83.3%, and accuracy of 91.7%. CONCLUSIONS: We have mainly described the radiological findings of pancreatic MiNEN with ill-defined irregular margin and moderate enhancement compared with PDAC and NET. The combination of imaging features could improve diagnostic efficiency and help in the selection of the correct treatment method.

External auditory canal and middle ear tumors: characterization by morphology and diffusion features on CT and MRI.

PURPOSE: To explore the value of morphology and diffusion features on CT and MRI in the characterization of external auditory canal and middle ear tumors (EAMETs). METHODS: Forty-seven patients with histologically proved EAMETs (23 benign and 24 malignant) who underwent CT and MRI were retrospectively analyzed in this study. CT and MRI characteristics (including size, shape, signal intensity, border, enhancement degree, and bone changes) and apparent diffusion coefficient (ADC) value were analyzed and compared between benign and malignant EAMETs. Logistic regression, receiver operating characteristic (ROC) curve, and Delong test were performed to assess the diagnostic performance. RESULTS: Compared with benign tumors, the malignant EAMETs are characterized by irregular shape, ill-defined border, invasive bone destruction, and intense enhancement (all p < 0.05). There were no significant differences on the size and signal intensity between benign and malignant tumors. The ADC value of malignant tumors were (879.96 ± 201.15) × 10-6 mm2/s, which was significantly lower than benign ones (p < 0.05). Logistic regression demonstrates the presence of ill-defined margin, invasive bone destruction, and low ADC value (≤ 920.33 × 10-6 mm2/s) have significant relationship with malignant EAMETs. The combination of characterization by morphology and diffusion features on CT and MRI can further improve the diagnostic efficiency when compared with morphology and diffusion features alone (both p < 0.05). CONCLUSION: Some CT and MRI characteristics are helpful in identifying malignant EAMETs from benign ones (especially ill-defined margin, invasive bone destruction, and low ADC value), and the combination of morphology and diffusion features on CT and MRI has best diagnostic efficiency for discriminating these two entities.

Synthetic phase-sensitive inversion-recovery vessel for assessing extramural venous invasion in patients with rectal cancer: imaging quality and added value to T2-wighted imaging.

OBJECTIVES: To evaluate the imaging quality of a synthetic phase-sensitive inversion recovery (SyPSIR) vessel and to add value to T2-weighted imaging (T2WI) for extramural venous invasion (EMVI) detection in patients with rectal cancer. METHODS: Participants in this retrospective study underwent preoperative synthetic MRI between October 2020 and April 2022. SyPSIR image reconstruction was performed with a single inversion time of 10 ms. A junior and a senior radiologist evaluated the imaging quality, including overall imaging quality scores, motion artifact scores, and relative image signal intensity contrast between the tumor and peritumoral vessels (SItumor-vessel), of both T2WI and SyPSIR vessels. Differences in imaging quality between the two methods were assessed using the Wilcoxon signed-rank test and two-sample t-test. EMVI scores were recorded for T2WI and T2WI+SyPSIR vessel. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the diagnostic performance. RESULTS: A total of 106 patients (35 EMVI+ and 71 EMVI-) were evaluated. There were no statistically significant differences in the overall image quality scores, motion artifacts, or SItumor-vessel (p = 0.08-0.93) between the T2WI and SyPSIR vessels. On combining T2WI and SyPSIR vessels, the AUC for pathological EMVI+ diagnoses increased from 0.65 to 0.88 for the junior radiologist and from 0.86 to 0.96 for the senior radiologist. Furthermore, the sensitivity of the analyses by junior and senior radiologists increased from 0.40 to 0.77 and 0.49 to 0.86, respectively. CONCLUSION: A SyPSIR vessel can provide additional information to improve the diagnostic efficiency of pathological EMVI in rectal cancer, which may be beneficial for individualized clinical treatment. KEY POINTS: • SyPSIR vessel and T2WI had similar imaging quality. • EMVI evaluation in SyPSIR vessel has a high inter-observer agreement. • The SyPSIR vessel has the potential to improve the diagnostic efficiency of EMVI detection in rectal cancer.

Prediction of response to neoadjuvant chemotherapy in advanced gastric cancer: A radiomics nomogram analysis based on CT images and clinicopathological features.

PURPOSE: To investigate the feasibility of predicting the early response to neoadjuvant chemotherapy (NAC) in advanced gastric cancer (AGC) based on CT radiomics nomogram before treatment. MATERIALS AND METHODS: The clinicopathological data and pre-treatment portal venous phase CT images of 180 consecutive AGC patients who received 3 cycles of NAC are retrospectively analyzed. They are randomly divided into training set (n = 120) and validation set (n = 60) and are categorized into effective group (n = 83) and ineffective group (n = 97) according to RECIST 1.1. Clinicopathological features are compared between two groups using Chi-Squared test. CT radiomic features of region of interest (ROI) for gastric tumors are extracted, filtered and minimized to select optimal features and develop radiomics model to predict the response to NAC using Pyradiomics software. Furthermore, a nomogram model is constructed with the radiomic and clinicopathological features via logistic regression analysis. The receiver operating characteristic (ROC) curve analysis is used to evaluate model performance. Additionally, the calibration curve is used to test the agreement between prediction probability of the nomogram and actual clinical findings, and the decision curve analysis (DCA) is performed to assess the clinical usage of the nomogram model. RESULTS: Four optimal radiomic features are selected to construct the radiomics model with the areas under ROC curve (AUC) of 0.754 and 0.743, sensitivity of 0.732 and 0.750, specificity of 0.729 and 0.708 in the training set and validation set, respectively. The nomogram model combining the radiomic feature with 2 clinicopathological features (Lauren type and clinical stage) results in AUCs of 0.841 and 0.838, sensitivity of 0.847 and 0.804, specificity of 0.771 and 0.794 in the training set and validation set, respectively. The calibration curve generates a concordance index of 0.912 indicating good agreement of the prediction results between the nomogram model and the actual clinical observation results. DCA shows that patients can receive higher net benefits within the threshold probability range from 0 to 1.0 in the nomogram model than in the radiomics model. CONCLUSION: CT radiomics nomogram is a potential useful tool to assist predicting the early response to NAC for AGC patients before treatment.

Synthetic MRI in differentiating benign from metastatic retropharyngeal lymph node: combination with diffusion-weighted imaging.

OBJECTIVES: This study aimed to evaluate the synthetic MRI (syMRI), its combination with diffusion-weighted imaging (DWI), and morphological features for discriminating benign from metastatic retropharyngeal lymph nodes (RLNs). METHODS: Fifty-eight patients with a total of 63 RLNs (21 benign and 42 metastatic) were enrolled. The mean and standard deviation of syMRI-derived relaxometry parameters (T1, T2, PD; T1SD, T2SD, PDSD) were obtained from two different regions of interest (namely, partial-lesion and full-lesion ROI). The parameters derived from benign and metastatic RLNs were compared using Student's t or chi-square tests. Logistic regression analysis was used to construct a multi-parameter model of syMRI, syMRI + DWI, and syMRI + DWI + morphological features. Areas under the curve (AUC) were compared using the DeLong test to determine the best diagnostic approach. RESULTS: Benign RLNs had significantly higher T1, T2, PD, and T1SD values compared with metastatic RLNs in both partial-lesion and full-lesion ROI (all p < 0.05). The T1SD obtained from full-lesion ROI showed the best diagnostic performance among all syMRI-derived single parameters. The AUC of combined syMRI multiple parameters (T1, T2, PD, T1SD) were higher than those of any single parameter from syMRI. The combination of synthetic MRI and DWI can improve the AUC regardless of ROI delineation. Furthermore, the combination of synthetic MRI, DWI-derived quantitative parameters, and morphological features can significantly improve the overall diagnostic performance. CONCLUSIONS: The value of syMRI has been validated in differential diagnosis of benign and metastatic RLNs, and syMRI + DWI + morphological features can further improve the diagnostic efficiency for discriminating these two entities. KEY POINTS: • Synthetic MRI was useful in differential diagnosis of benign and metastatic RLNs. • The combination of syMRI, DWI, and morphological features can significantly improve the diagnostic efficiency.

Tuning oxygen vacancies in epitaxial LaInO3 films for ultraviolet photodetection.

LaInO3 (LIO) represents a new, to the best of knowledge, type of perovskite oxides for deep-ultraviolet (DUV) photodetection owing to the wide bandgap nature (∼5.0 eV) and the higher tolerance of defect engineering for tunable carrier transport. Here we fabricate fast-response DUV photodetectors based on epitaxial LIO thin films and demonstrate an effective strategy for balancing the photodetector performance using the oxygen growth pressure as a simple control parameter. Increasing the oxygen pressure is effective to suppress the oxygen vacancy formation in LIO, which is beneficial to suppress the dark current and enhance the response speed. The optimized LIO photodetector achieves a fast rise/fall time of 20 ms/73 ms, a low dark current of 2.0 × 10-12 A, a photo-to-dark current ratio of 1.2 × 103, and a detectivity of 6 × 1012 Jones.

Differential diagnosis of diffuse sclerotic thyroid papillary carcinoma and Hashimoto's thyroiditis using fine-needle aspiration cytology, BRAFV600E , and ultrasound elastography.

BACKGROUND: The diffuse sclerosing variant of papillary thyroid carcinoma (DSV-PTC) has ultrasound findings that are similar to Hashimoto's thyroiditis (HT), resulting in under-diagnosis. DSV-PTC combined with HT is also common, so early and accurate diagnosis of DSV-PTC using a variety of diagnostic techniques, including FNAC, BRAFV600E mutation detection, and ultrasound elastography, is critical. OBJECTIVE: To assess the diagnostic value of fine-needle aspiration cytology (FNAC) and BRAFV600E detection in combination with ultrasound elastography in the diagnosis of DSV-PTC. METHODS: We performed a retrospective analysis of 40 patients with pathologically confirmed DSV-PTC and 43 patients with HT admitted to our hospital's ultrasound department between January 2015 and December 2020. Preoperative FNAC, BRAFV600E mutation detection, and ultrasound elastography imaging were all performed on all patients. For a definitive diagnosis, the results of these tests were compared to postoperative pathological findings. The diagnostic value of FNAC, BRAFV600E mutation detection, ultrasound elasticity imaging, and their combination for DSV-PTC diagnosis was assessed. RESULTS: The mean elastic strain rate ratio (E1/E2) of the 40 DSV-PTC cases was 5.75 ± 2.14, while that of the 43 HT cases was 2.81 ± 1.20. The receiver operating characteristic (ROC) curve was generated using the average value of E2/E1. The area under the ROC curve was 0.910, and the optimal E2/E1 cut-off value was 4.500. When FNAC, BRAFV600E mutation detection, and ultrasound elasticity imaging detection were combined, the diagnostic sensitivity, specificity, negative predictive value, positive predictive value, and accuracy of DSV-PTC diagnosis were 92.5%, 95.3%, 93.2%, 94.9%, and 94.0%, respectively, which were significantly higher than the single technique (p < 0.05). CONCLUSIONS: The use of FNAC, BRAFV600E mutation detection, and ultrasound elastography in combination is more helpful in establishing an accurate diagnosis of DSV-PTC than using a single diagnostic technique alone.

Radiomic Model for Determining the Value of Elasticity and Grayscale Ultrasound Diagnoses for Predicting BRAFV600E Mutations in Papillary Thyroid Carcinoma.

BRAFV600E is the most common mutated gene in thyroid cancer and is most closely related to papillary thyroid carcinoma(PTC). We investigated the value of elasticity and grayscale ultrasonography for predicting BRAFV600E mutations in PTC. Methods: 138 patients with PTC who underwent preoperative ultrasound between January 2014 and 2021 were retrospectively examined. Patients were divided into BRAFV600E mutation-free group (n=75) and BRAFV600E mutation group (n=63). Patients were randomly divided into training (n=96) and test (n=42) groups. A total of 479 radiomic features were extracted from the grayscale and elasticity ultra-sonograms. Regression analysis was done to select the features that provided the most information. Then, 10-fold cross-validation was used to compare the performance of different classification algorithms. Logistic regression was used to predict BRAFV600E mutations. Results: Eight radiomics features were extracted from the grayscale ultrasonogram, and five radiomics features were extracted from the elasticity ultrasonogram. Three models were developed using these radiomic features. The models were derived from elasticity ultrasound, grayscale ultrasound, and a combination of grayscale and elasticity ultrasound, with areas under the curve (AUC) 0.952 [95% confidence interval (CI), 0.914-0.990], AUC 0.792 [95% CI, 0.703-0.882], and AUC 0.985 [95% CI, 0.965-1.000] in the training dataset, AUC 0.931 [95% CI, 0.841-1.000], AUC 0. 725 [95% CI, 0.569-0.880], and AUC 0.938 [95% CI, 0.851-1.000] in the test dataset, respectively. Conclusion: The radiomic model based on grayscale and elasticity ultrasound had a good predictive value for BRAFV600E gene mutations in patients with PTC.

Assessing Diagnostic Value of Combining Ultrasound and MRI in Extrathyroidal Extension of Papillary Thyroid Carcinoma.

Purpose: To explore the separate diagnostic value of preoperative ultrasound (US), magnetic resonance imaging (MRI), and the combination of US and MRI in extrathyroidal extension (ETE) of papillary thyroid carcinoma (PTC). Materials and Methods: This retrospective study was approved by the Affiliated People's Hospital of Jiangsu University review board. A total of 158 PTC patients with ETE received preoperative US and MRI examination and underwent surgery between May 2014 and December 2018 in Affiliated People's Hospital of Jiangsu University. For each case, the US and MRI features of ETE were retrospectively and independently investigated by two radiologists. The clinical assessment for each case was implemented, respectively, using US imaging only, MRI only, and a combination of both modalities at three different time points with one-month intervals. Results: The diagnostic accuracies of US, MRI, and the combined set for T3 (minimal ETE) were 91.7% (88/96), 74.0% (71/96), and 97.9% (94/96), respectively, indicating a significantly different performance (P < 0.001). The diagnostic accuracies for T4 (extensive ETE) were 62.9% (39/62), 87.1% (54/62), and 93.5% (58/62), respectively. The difference between the three methods for T4 was statistically significant (P = 0.000). The diagnostic accuracies for overall ETE were 80.4% (127/158), 79.1% (125/158), and 96.2% (152/158), respectively. The difference between the three methods for ETE was statistically significant (P = 0.001). Conclusion: This study suggests that ETE can be predicted most accurately by the combination of preoperative US and MRI.

Multiple detection of 15 triazine herbicides by gold nanoparticle based-paper sensor.

Triazine herbicides have been widely used in agriculture, but their residues can harm the environment and human health. To help monitor these, we have developed an effective immunochromatographic strip test that can simultaneously detect 15 different triazines in grain samples (including ametryn, cyprazine, atraton, prometon, prometryn, atrazine, propazine, terbuthylazine, simetryn, trietazine, secbumeton, simazine, desmetryn, terbumeton and simetone). Based on our optimization procedure, the visual limit of detection (vLOD) for these triazines was found to be 2-10 ng/mL in assay buffer, and 0.02-0.1 mg/kg in grain samples. Four different grain matrices including corn, brown rice, wheat, and sorghum were studied and the test results showed no significant differences between the 15 triazines analyzed using this method. This test is simple, convenient, rapid, and low-cost, and could be an effective tool for primary screening of triazine residues in grain samples. Electronic Supplementary Material: Supplementary material (UV-vis spectra of 15 nm-GNP; K2CO3 usage; cross reactivity; strip images for spiked rice, wheat and sorghum samples, UPLC-MS/MS parameters; gray values for strip optimization) is available in the online version of this article at 10.1007/s12274-022-4164-2.