[The derivation and validation of a prediction rule for differential diagnosis of thyroid nodules].

OBJECTIVE: To set up a prediction rule for the pro-operative differential diagnosis of thyroid nodules and evaluate its clinical value. METHODS: All patients of thyroid nodules underwent thyroid operations in Changzheng hospital from June, 1997 to July, 2012 were included in this study. They were randomly divided into the derivation cohort (2/3) and the validation cohort (1/3). A prediction rule was developed based on the logistic regression model and the scoring system was established in accordance with assigning of the value of each variable ß in the model. The prediction consistency, discriminatory power and diagnostic accuracy were conducted to evaluate the clinical value of the scoring system. RESULTS: A total of 13 980 patients were enrolled in the study with 9195 in the derivation cohort and 4785 in the validation cohort. The prediction rule consisted of 18 variables, which were gender, clinical manifestations including fever, neck sore, neck mass, palpitation and sweating, serum level of thyroid stimulating hormone (TSH) , free triiodothyronine (FT3) , thyroid peroxidase antibody (TPOAb) , thyroglobulin antibody (TgAb) , thyroglobulin (Tg) , calcitonin and carcinoembryonic antigen (CEA) , ultrasonography features including nodules number, location, size, boundaries and ethological patterns and the presence and patterns of lymph nodes. The model showed good calibration consistency (P = 0.437) and discrimination power (area under the receiver operating characteristic curve was 0.928) in the derivation cohort. The sensitivity, specificity, accuracy, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio of the model were 89.3%, 81.5%, 83.2%, 56.8%, 96.6%, 4.83 and 0.13, respectively. CONCLUSION: The prediction rule and its scoring system established in the study are efficacious for the calibration and discrimination of thyroid nodules in Chinese population, which could be a useful tool for the pro-operative risk stratification.

A deep learning-based diagnostic pattern for ultrasound breast imaging: can it reduce unnecessary biopsy?

Background: Early studies have demonstrated the potential of deep learning in bringing revolutionary changes in medical analysis. However, it is unknown which deep learning based diagnostic pattern is more effective for differentiating malignant and benign breast lesions (BLs) and can assist radiologists to reduce unnecessary biopsies. Methods: A total of 506 malignant BLs and 557 benign BLs were enrolled in this study after excluding incomplete ultrasound images. 396 malignant BLs and 447 benign BLs were included in the training cohort while 110 malignant and 110 benign BLs were included in the validation cohort. All BLs in the training and validation cohort were biopsy-proven. The most common convolutional neural networks (VGG-16 and VGG-19) were applied to identify malignant and benign BLs using grey-scale ultrasound images. Two radiologists determined the malignant (suggestion for biopsy) and benign (suggestion for follow-up) BLs with a 2-step reading session. The first step was based on conventional ultrasound (US) images alone to make a biopsy or follow-up decision. The second step was to take deep learning results into account for the decision adjustment. If a deep learning result of a first-classified benign BL was above the cut-off value, then it was re-classified as malignant. Results: In terms of area under the curve (AUC), the VGG-19 model yielded the best diagnostic performance in both training [0.939, 95% confidence interval (CI): 0.924-0.954] and testing dataset (0.959, 95% CI: 0.937-0.982). With the aid of deep learning models, the AUC of radiologists improved from 0.805 (95% CI: 0.744-0.865) to 0.827 (95% CI: 0.771-0.875, VGG-16) and 0.914 (95% CI: 0.871-0.957, VGG-19). The unnecessary biopsies decreased from 10.0% (11/110) to 8.2% (9/110) (assisted by VGG-16) and 0.9% (1/110) (assisted by VGG-19). Conclusions: The application of deep learning patterns in breast US may improve the diagnostic performance of radiologists by offering a second opinion. And thus, the assist of deep learning algorithm can considerably reduce the unnecessary biopsy rate in the clinical management of breast lesions.

Bufalin stimulates antitumor immune response by driving tumor-infiltrating macrophage toward M1 phenotype in hepatocellular carcinoma.

BACKGROUND: Immunotherapy for hepatocellular carcinoma (HCC) exhibits limited clinical efficacy due to immunosuppressive tumor microenvironment (TME). Tumor-infiltrating macrophages (TIMs) account for the major component in the TME, and the dominance of M2 phenotype over M1 phenotype in the TIMs plays the pivotal role in sustaining the immunosuppressive character. We thus investigate the effect of bufalin on promoting TIMs polarization toward M1 phenotype to improve HCC immunotherapy. METHODS: The impact of bufalin on evoking antitumor immune response was evaluated in the immunocompetent mouse HCC model. The expression profiling of macrophage-associated genes, surface markers and cytokines on bufalin treatment in vitro and in vivo were detected using flow cytometry, immunofluorescence, western blot analysis, ELISA and RT-qPCR. Cell signaling involved in M1 macrophage polarization was identified via the analysis of gene sequencing, and bufalin-governed target was explored by immunoprecipitation, western blot analysis and gain-and-loss of antitumor immune response. The combination of bufalin and antiprogrammed cell death protein 1 (anti-PD-1) antibody was also assessed in orthotopic HCC mouse model. RESULTS: In this study, we showed that bufalin can function as an antitumor immune modulator that governs the polarization of TIMs from tumor-promoting M2 toward tumor-inhibitory M1, which induces HCC suppression through the activation of effector T cell immune response. Mechanistically, bufalin inhibits overexpression of p50 nuclear factor kappa B (NF-κB) factor, leading to the predominance of p65-p50 heterodimers over p50 homodimers in the nuclei. The accumulation of p65-p50 heterodimers activates NF-κB signaling, which is responsible for the production of immunostimulatory cytokines, thus resulting in the activation of antitumor T cell immune response. Moreover, bufalin enhances the antitumor activity of anti-PD-1 antibody, and the combination exerts synergistic effect on HCC suppression. CONCLUSIONS: These data expound a novel antitumor mechanism of bufalin, and facilitate exploitation of a new potential macrophage-based HCC immunotherapeutic modality.

Quantitative evaluation of striated muscle injury by multiscale blob features method.

PURPOSE: This study aimed to use the multiscale blob feature (MBF) method to quantitatively evaluate porcine striated muscle injuries. METHODS: A porcine striated muscle injury model was induced by microwave ablation and anhydrous acetic acid injection, respectively. Then, both 2D sonographic and histological features of the lesions were recorded and compared. Later, MBF was used to quantitatively evaluate the porcine striated muscle injuries by extracting the texture features from the 2D ultrasonogram via measuring eight textural parameters (Mean, SDev, NOB, [Formula: see text], [Formula: see text], HOD, DOD, and POD). RESULTS: Microwave ablation produced oval or round-like lesions, which had a pale gray color, with an echo attenuation detected at lesion center due to carbonization; anhydrous acetic acid injection produced long, stripped lesions, which had a slate-gray color, with a gas-like intense echo at lesion center. There were significant differences in Mean, [Formula: see text] and POD between the muscle samples treated by microwave ablation and the control samples, as well as significant differences in NOB, [Formula: see text] and POD between the muscle samples treated by anhydrous acetic acid injection and the control. There were significant differences in Mean, [Formula: see text], NOB, and [Formula: see text] between the muscle samples treated by microwave ablation and those treated by anhydrous acetic acid injection. CONCLUSION: Quantitative evaluation of striated muscle injuries using the MBF method was able to differentiate the muscle injuries caused by microwave ablation and anhydrous acetic acid injection, suggesting that this method may be a potential and reliable tool for quantitative evaluation of muscle injuries.

Monitoring of heavy metal levels in the major rivers and in residents' blood in Zhenjiang City, China, and assessment of heavy metal elimination via urine and sweat in humans.

The coastal areas of China face great challenges, owing to heavy metal contamination caused by rapid industrialization and urbanization. To our knowledge, this study is the first report of the levels of heavy metals in the major rivers of Zhenjiang, one of the most important cities of the Yangtze River Delta in China. In addition, we measured heavy metal levels in the blood of 76 residents of Zhenjiang. The results suggest that the presence of heavy metals in the blood may threaten human health and the distribution appeared to correspond to most highly populated areas and/or areas with high traffic. We also found that the concentration of heavy metals in human blood showed an accumulation effect with increase in age. Moreover, the levels of most heavy metals were lower in participants who regularly exercised than in those who did not. We studied heavy metal levels in the urine and sweat of another 17 volunteers to monitor the elimination of bioaccumulated heavy metal. Heavy metals were found in the urine and sweat of all the 17 participants and were more concentrated in sweat. Induced micturition and sweating appear to be potential methods for the elimination of heavy metals from the human body.

A new computational model for human thyroid cancer enhances the preoperative diagnostic efficacy.

Considering the high rate of missed diagnosis and delayed treatments for thyroid cancer, an effective systematic model for the differential diagnosis is highly needed. Thus we analyzed the data on the clinicopathological characteristics, routine laboratory tests and imaging examinations in a cohort of 13,980 patients with thyroid cancer to establish a new diagnostic model for differentiating thyroid cancer in clinical practice. Here, we randomly selected two-thirds of the population to develop the thyroid malignancy risk scoring system (TMRS) for preoperative differentiation between thyroid cancer and benignant thyroid diseases, and then validated its differential diagnostic power in the rest one-third population. The 18 predictors finally enrolled in the TMRS included male gender, clinical manifestations (fever, neck sore, neck lump, palpitations or sweating), laboratory findings (TSH>1.56mIU/L, FT3>5.85pmol/L, TPOAb>14.97IU/ml, TgAb>48.00IU/ml, Tg>34.59µg/L, Ct>64.00ng/L, and CEA>0.41µg/L), and ultrasound features (tumor number≤ 23mm, site, size, echo texture, margins, and shape of neck lymphnodes). The TMRS is validated to be well-calibrated (P = 0.437) and excellently discriminated (AUC = 0.93, 95% CI [0.92, 0.94]), with an accuracy of 83.2%, a sensitivity of 89.3%, a specificity of 81.5%, positive and negative predictive values of 56.8% and 96.6%, positive and negative likelihood ratios of 4.83 and 0.13 in the development cohort, respectively. The TMRS highlights that this differential diagnostic system could help provide accurate preoperative risk stratification for thyroid cancer, and avoid unnecessary over- and under-treatment for such patients.