Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning.

To investigate the ability of an auxiliary diagnostic model based on the YOLO-v7-based model in the classification of cervical lymphadenopathy images and compare its performance against qualitative visual evaluation by experienced radiologists. Three types of lymph nodes were sampled randomly but not uniformly. The dataset was randomly divided into for training, validation, and testing. The model was constructed with PyTorch. It was trained and weighting parameters were tuned on the validation set. Diagnostic performance was compared with that of the radiologists on the testing set. The mAP of the model was 96.4% at the 50% intersection-over-union threshold. The accuracy values of it were 0.962 for benign lymph nodes, 0.982 for lymphomas, and 0.960 for metastatic lymph nodes. The precision values of it were 0.928 for benign lymph nodes, 0.975 for lymphomas, and 0.927 for metastatic lymph nodes. The accuracy values of radiologists were 0.659 for benign lymph nodes, 0.836 for lymphomas, and 0.580 for metastatic lymph nodes. The precision values of radiologists were 0.478 for benign lymph nodes, 0.329 for lymphomas, and 0.596 for metastatic lymph nodes. The model effectively classifies lymphadenopathies from ultrasound images and outperforms qualitative visual evaluation by experienced radiologists in differential diagnosis.

Ultrasound combined with Ki-67 to construct the prognostic model for radioactive iodine therapy outcomes in Graves' disease patients.

The aim of this study was to develop a prognostic model for radioactive iodine (RAI) therapy outcome in patients with Graves' disease. We enrolled 127 patients. Information on RAI therapy, ultrasound indexes of thyroid, and other lifestyle factors was collected. The competing risk model was used to estimate the multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for nonhealing or recurrence of hyperthyroidism (NHRH). The performance of the model was assessed by receiver operator characteristic analysis and the Brier score and internally validated by bootstrap resampling. Then, a nomogram was developed. Forty-one cases (32.2%) of NHRH were documented. Positive Ki-67 expression, a higher dose of per-unit thyroid volume, and females showed lower risks of NHRH (all P < 0.05). The HR values (95% CI) were 0.42 (0.23, 0.79), 0.01 (0.00, 0.02), and 0.47 (0.25, 0.89), respectively. The bootstrap validation showed that the model had the highest accuracy and good calibration for predicting cumulative risk of NHRH at 180 days after RAI therapy (AUC = 0.772; 95% CI: 0.640-0.889, Brier score = 0.153). By decision curve analysis, the nomogram was shown to have a satisfactory net benefit between thresholds of 0.20 and 0.40. Ki-67, ultrasound volumetry, and scintigraphy techniques can play important roles in evaluating RAI therapy outcome in Graves' disease patients. The prediction nomogram shows reasonable accuracy in predicting NHRH.

Prediction of papillary thyroid metastases to the central compartment: proposal of a model taking into consideration other thyroid conditions.

Objective: To construct risk prediction models for cervical lymph node metastasis (CLNM) of papillary thyroid carcinoma (PTC) under different thyroid disease backgrounds and to analyze and compare risk factors among different groups. Methods: This retrospective study included 518 patients with PTC that was pathologically confirmed post-operatively from January 2021 to November 2021. Demographic, ultrasound and pathological data were recorded. Univariate and multivariate logistic regression analyses were performed to identify factors associated with CLNM in the whole patient cohort and in patients grouped according to diagnoses of Hashimoto's thyroiditis (HT), nodular goiter (NG), and no background disease. Prediction models were constructed for each group, and their performances were compared. Results: Analysis of the whole PTC patient cohort identified NG as independently associated with CLNM. The independent risk factors for patients with no background disease were the maximum thyroid nodule diameter and American College of Radiology Thyroid Imaging Reporting & Data System score; those for patients with HT were the maximum thyroid nodule diameter, ACR TI-RADS score, and multifocality; and those for patients with NG were the maximum thyroid nodule diameter, ACR TI-RADS score, multifocality and gender. Conclusion: Background thyroid disease impacts CLNM in PTC patients, and risk factors for CLNM vary among PTC patients with different background diseases. Ultrasound is useful for diagnosing background thyroid disease, which can inform treatment planning. Different prediction models are recommended for PTC cases with different thyroid diseases.

Suitability evaluation and potential estimation of photovoltaic power generation and carbon emission reduction in the Qinghai-Tibet Plateau.

The expansion of power development industry is facing enormous pressure to reduce carbon emissions in the context of global decarbonization. Using solar energy instead of traditional fossil energy to adjust energy structure is one of the important means for reducing carbon emissions. Existing research focuses on the evaluation of the generation potential of centralized or distributed photovoltaic power plants, rather than the comprehensive evaluation of multi-type power plants. Based on multi-source remote sensing data for information extraction and suitability evaluation, this paper develops a method to comprehensively evaluate the construction potential of multi-type photovoltaic power stations and determine the potential of photovoltaic power generation and carbon emission reduction on the Qinghai-Tibet Plateau (QTP). The results showed that estimating the power generation potential of only single-type photovoltaic power stations cannot accurately reflect the photovoltaic power generation potential of QTP. It is also demonstrated that the emission reduction effect of the photovoltaic power generation in all prefecture-level cities of QTP can meet national emission reduction targets, showing high annual power generation potential, of which 86.59% is concentrated in Qinghai province's Guoluo, Yushu, and Haixi. An accurate estimation of the photovoltaic power generation potential in QTP can provide a useful theoretical basis for developing carbon-saving and emission reduction strategies for clean energy in China.

Construction and Validation of a Predictive Nomogram Based on Ultrasound for Lymph Node Metastasis of Papillary Thyroid Carcinoma in the Cervical Central Region.

ABSTRACT: To establish and validate a nomogram for predicting lymph node metastasis (LNM) of papillary thyroid carcinoma (PTC) in the cervical central region. This retrospective study included 287 PTC patients with 309 nodules treated from December 2018 to May 2020 at our hospital. The cohort was divided randomly into a training set and a testing set according to a 7:3 ratio. The training set contained 216 nodules, and the testing set contained 93 nodules. The nomogram was developed using the training set, and the data of the testing set were used to validate the performance of nomogram. The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index) and calibration curve. The study showed multifocality, thyroid lesion size, and American College of Radiology Thyroid Imaging, Reporting and Data System (TI-RADS) score were significantly independently associated with LNM in the cervical central region. In the testing set, the calibration curve showed that the nomogram had good discrimination with a C-index of 0.775 (95% confidence interval, 0.680-0.869) and adequate calibration ( P = 0.808). By decision curve analysis and clinical impact curve analysis, the nomogram was shown to have a satisfactory net benefit between thresholds of 0.40 and 0.75. The nomogram can be used for predicting LNM of PTC in the cervical central region and may provide valuable guidance for planning the surgical treatment of PTC patients.

Calcification, Posterior Acoustic, and Blood Flow: Ultrasonic Characteristics of Triple-Negative Breast Cancer.

Previous studies suggest that triple-negative breast cancer (TNBC) may have unique imaging characteristics, however, studies focused on the imaging characteristics of TNBC are still limited. The aim of the present study is to analyze the ultrasonic characteristics of TNBC and to provide more reliable information on imaging diagnosis of TNBC. This retrospective study was performed including 162 TNBC patients with 184 TNBC lesions. 174 non-TNBC cases with 196 lesions were used as the control group. The median size of TNBC lesions and non-TNBC lesions were 23 mm × 16 mm and 21 mm × 15 mm, respectively. The shape of most breast cancer lesions was irregular. However, 15.30% (28/183) TNBC lesions and 16.84% (33/196) non-TNBC lesions were oval-shaped. Most breast cancer lesions (79.78% TNBC & 85.71% non-TNBC) were ill-defined. In comparison to non-TNBC, the distinctive ultrasonic characteristics of TNBC were summarized as three features: calcifications, posterior acoustic, and blood flow. Microcalcifications was less common in non-TNBC. The remarkable posterior acoustic characteristics on TNBC were no posterior acoustic features (136, 73.91%). Avascular pattern (21.74%) was also more common in TNBC. The other feature of TNBC was markedly hypoechoic lesions (23.91%). The above-mentioned differences between TNBC and non-TNBC were significant. 93.48% TBNC and 94.39% non-TNBC lesions were in BI-RADS-US category of 4A-5. The results indicate that TNBC has some distinctive ultrasound characteristics. Ultrasound is a useful adjunct in early detection of breast cancer. A combination of ultrasound with mammography is excellent for detecting breast cancer.

[Establishment of a predictive model for central cervical lymph node metastasis of papillary thyroid carcinoma based on ACR TI-RADS score and evaluation of its diagnostic efficacy].

Objective:To establish a predictive model for central lymph node metastasis（CLNM） of papillary thyroid carcinoma（PTC） based on ACR TI-RADS grades（ATR model） and evaluate its diagnostic efficacy. Methods:A total of 319 patients with PTC diagnosed from January 2019 to May 2020 were included, including 366 nodules were used as the modeling cohort to construct the risk prediction model. A total of 105 PTC patients with 121 nodules from June to August 2020 were included as the external validation cohort. The C-index of the model was calculated and the Hosmer-Lemeshow goodness-of-fit test was performed to compare the diagnostic efficiency of ACR model and those conventional imaging models. Results:The ATR model, Y=-3.719+0.765×gender+1.094×multifocality+0.08×maximum diameter+0.266×ACR TI-RADS score. In the training set, validation set and external validation cohort, the model C-index was 0.758（95%CI: 0.699-0.817）, 0.717（95%CI: 0.619-0.815） and 0.756（95%CI: 0.671-0.840）, respectively. The Hosmer-Lemeshow goodness of fit test showed that the prediction rate of the model was consistent with the actual incidence rate（P=0.918; P=0.581; P=0.366）. With ≥0.434 as the diagnostic threshold, the model had the highest diagnostic efficacy （sensitivity: 86.0%, specificity: 56.3%, Youden index: 0.423）. In the external validation cohort, there was no significant difference between C-US and CT（P>0.05）. Compared with C-US and CT, the sensitivity（66.1% vs 16.1%, P<0.001; 66.1% vs 9.7%, P<0.001） and accuracy（68.6% vs 55.4%, P=0.041; 68.6% vs 52.9%, P=0.012） of ATR model were higher, and the negative predictive value was higher than that of CT（66.7% vs 50.9%, P=0.042）, but there was no difference between ATR model and C-US（66.7% vs 52.3%, P=0.066）; There was no significant difference among the three positive predictive values（70.7% vs 83.3%, P=0.211; 70.7% vs 85.7%, P=0.319）, but the specificity of the model was low （71.2% vs 96.6%, P=0.001; 71.2% vs 98.3%, P<0.001）. Conclusion:The predictive model based on ACR TI-RADS grades can predict CLNM of PTC more accurately and sensitively than traditional imaging examination.

Recognition of Thyroid Ultrasound Standard Plane Images Based on Residual Network.

Ultrasound is one of the critical methods for diagnosis and treatment in thyroid examination. In clinical application, many reasons, such as large outpatient traffic, time-consuming training of sonographers, and uneven professional level of physicians, often cause irregularities during the ultrasonic examination, leading to misdiagnosis or missed diagnosis. In order to standardize the thyroid ultrasound examination process, this paper proposes using a deep learning method based on residual network to recognize the Thyroid Ultrasound Standard Plane (TUSP). At first, referring to multiple relevant guidelines, eight TUSP were determined with the advice of clinical ultrasound experts. A total of 5,500 TUSP images of 8 categories were collected with the approval and review of the Ethics Committee and the patient's informed consent. Then, after desensitizing and filling the images, the 18-layer residual network model (ResNet-18) was trained for TUSP image recognition, and five-fold cross-validation was performed. Finally, through indicators like accuracy rate, we compared the recognition effect of other mainstream deep convolutional neural network models. Experimental results showed that ResNet-18 has the best recognition effect on TUSP images with an average accuracy rate of 91.07%. The average macro precision, average macro recall, and average macro F1-score are 91.39%, 91.34%, and 91.30%, respectively. It proves that the deep learning method based on residual network can effectively recognize TUSP images, which is expected to standardize clinical thyroid ultrasound examination and reduce misdiagnosis and missed diagnosis.

Diagnostic Performance of Ultrasound Computer-Aided Diagnosis Software Compared with That of Radiologists with Different Levels of Expertise for Thyroid Malignancy: A Multicenter Prospective Study.

The aim of the work described here was to evaluate the diagnostic performance of ultrasound thyroid computer-aided diagnosis (CAD) software. This multicenter prospective study included 494 patients (565 thyroid nodules) who underwent surgery or biopsy after ultrasonography at four hospitals from January 2019 to September 2019. The diagnostic performance metrics of different readers were calculated and compared with the pathologic results. The sensitivity of CAD was outstanding and was equivalent to that of a senior radiologist (90.51% vs. 88.47%, p > 0.05). The area under the curve of CAD was equivalent to that of a junior radiologist (0.748 vs. 0.739, p > 0.05). However, the specificity was only 49.63%, which was lower than those of the three radiologists (75.56%, 85.93% and 90.37% for the junior, intermediate and senior radiologists, respectively). The diagnostic performance of the junior radiologist was significantly improved with the aid of CAD (junior + CAD). The sensitivity and area under the curve of junior + CAD were improved from 72.20% to 89.93% and from 0.739 to 0.816, respectively (both p values <0.05), and the positive predictive value, negative predictive value and κ coefficient improved from 76.3% to 78.6%, 82.0% to 86.8% and 0.394 to 0.511, respectively. Though specificity slightly decreased from 75.56% to 73.33%, the difference was not statistically significant (p > 0.05). In general, the clinical application value of CAD is promising, and its instrumental value for junior radiologists is significant.

Noninvasive evaluation of gastric emptying and gastric wall thickness in SLE patients.

OBJECTIVE: The objective of this study is to evaluate the gastric emptying in patients with systemic lupus erythematosus (SLE) with gastrointestinal involvement using three-dimensional (3D) ultrasonography. METHODS: The gastric emptying times at 25% (T1), 50% (T2), and 75% (T3) of SLE patients with gastrointestinal involvement (n = 40) and healthy controls (n = 80) were evaluated and compared. In addition, the correlations among the gastric wall thickness, SLE disease activity index (SLEDAI), and upper gastrointestinal symptoms were calculated. RESULTS: The gastric wall thickness was correlated with the SLEDAI (r = 0.928, p < 0.001) and the upper gastrointestinal symptom index (r = 0.848, p < 0.001). The emptying times T1, T2, and T3 of the SLE patients were 17.08 ± 2.65 min (mean ± standard deviation), 39.85 ± 6.54 min, and 83.58 ± 7.12 min, respectively. For healthy controls, they were 19.65 ± 5.39 min, 41.08 ± 7.51 min, and 70.34 ± 8.03 min. The T1 of the SLE patients was shorter (p < 0.01), while the T3 was longer (p < 0.001). Moreover, T3 in the SLE group had the best correlation with the upper gastrointestinal symptom index (r = 0.553, p < 0.001). T1 in the SLE group was anti-correlated with early satiety (r = -0.366, p < 0.05). CONCLUSIONS: Combining the emptying times T1 and T3, as well as the gastric wall thickness, the SLEDAI and the upper gastrointestinal symptoms index can provide accurate clinical diagnosis of SLE with gastric involvement.

Evaluation of gastric blood supply in diabetic patients with gastroparesis by contrast-enhanced ultrasound.

OBJECTIVE: Improvement in regional blood flow has been shown to ameliorate diabetic gastroparesis. We compared the gastric blood supply in patients with diabetes with gastroparesis with that in healthy subjects, by using contrast-enhanced ultrasound (CEUS). METHODS: 30 healthy subjects and 40 patients with diabetic gastroparesis were enrolled. The CEUS parameters of greater curvatures of the antrum (GCOA) and lesser curvatures of the antrum (LCOA), including peak intensity (PI) and the area under the curve (AUC), were compared between the two groups. RESULTS: Intraclass correlation coefficient (ICC) for PI in healthy subjects measured on CEUS were 0.831-0.857 and 0.803-0.823, respectively. Intra-ICC and inter-ICC values for AUC were 0.805-0.823 and 0.813-0.815, respectively. In both groups, no significant difference was observed in PI and AUC values of GCOA and LCOA (p > 0.05). The PI and AUC of GCOA and LCOA in the diabetes group were less than those in the normal group (p < 0.05). CONCLUSION: CEUS can assess stomach wall vascularity with a high reproducibility. Microcirculation in the antrum of patients with diabetic gastroparesis is poorer than that of normal group, which is consistent with the mechanisms of diabetic neuropathy. CEUS can be used for evaluation of microvascular perfusion in patients with stomach wall disease. Advances in knowledge: This was the first study to use CEUS for assessment of blood supply of the gastric wall and to compare microvascular perfusion between healthy individuals and patients with diabetes with gastroparesis.

Evaluation of gastric emptying in diabetic gastropathy by an ultrasonic whole stomach cylinder method.

In order to explore the accuracy of ultrasonic whole stomach cylinder measurement (UWSCM) in the evaluation of gastric emptying, we measured the gastric emptying times (ET) at 25% (T1), 50% (T2) and 75% (T3) of healthy subjects and patients with diabetic gastropathy by UWSCM and scintigraphy. The ET of patients were compared with their clinical symptom scores. We found that the ET measured by UWSCM showed no significant difference with scintigraphy (p > 0.05). The correlation between them was good, and the correlation coefficient of T3 reached 0.744 (p < 0.05). All emptying times in the diabetic patients were longer than those in the healthy subjects (p < 0.05). The T3 in the diabetic group measured by UWSCM had the best correlation with the symptom index (r = 0.469, p < 0.05). We conclude that ET measured by UWSCM is accurate and T3 combining the symptoms index provides an accurate clinical basis for gastropathy.