Application of radiomics model based on ultrasound image features in the prediction of carpal tunnel syndrome severity.

OBJECTIVE: The aim of our study is to develop and validate a radiomics model based on ultrasound image features for predicting carpal tunnel syndrome (CTS) severity. METHODS: This retrospective study included 237 CTS hands (106 for mild symptom, 68 for moderate symptom and 63 for severe symptom). There were no statistically significant differences among the three groups in terms of age, gender, race, etc. The data set was randomly divided into a training set and a test set in a ratio of 7:3. Firstly, a senior musculoskeletal ultrasound expert measures the cross-sectional area of median nerve (MN) at the scaphoid-pisiform level. Subsequently, a recursive feature elimination (RFE) method was used to identify the most discriminative radiomic features of each MN at the entrance of the carpal tunnel. Eventually, a random forest model was employed to classify the selected features for prediction. To evaluate the performance of the model, the confusion matrix, receiver operating characteristic (ROC) curves, and F1 values were calculated and plotted correspondingly. RESULTS: The prediction capability of the radiomics model was significantly better than that of ultrasound measurements when 10 robust features were selected. The training set performed perfect classification with 100% accuracy for all participants, while the testing set performed accurate classification of severity for 76.39% of participants with F1 values of 80.00, 63.40, and 84.80 for predicting mild, moderate, and severe CTS, respectively. Comparably, the F1 values for mild, moderate, and severe CTS predicted based on the MN cross-sectional area were 76.46, 57.78, and 64.00, respectively.. CONCLUSION: This radiomics model based on ultrasound images has certain value in distinguishing the severity of CTS, and was slightly superior to using only MN cross-sectional area for judgment. Although its diagnostic efficacy was still inferior to that of neuroelectrophysiology. However, this method was non-invasive and did not require additional costs, and could provide additional information for clinical physicians to develop diagnosis and treatment plans.

A nomogram based on ultrasound characteristics to predict large-number cervical lymph node metastasis in papillary thyroid carcinoma.

To establish a nomogram for predicting large-number cervical lymph node metastases (LNMs) of primary papillary thyroid carcinoma (PTC) based on ultrasound characteristics. This retrospective study included patients with PTC diagnosed by pathological examination and who underwent surgery between August 2015 and May 2021 at Hwa Mei Hospital, University of Chinese Academy of Sciences (Ningbo, China). Large-number LNM was defined as >5 lymph nodes with metastases. The patients were propensity score-matched (PSM) for age and sex. A multivariable analysis was used to determine the risk factors for massive LNM. After PSM, the 78 patients with large-number LNM were matched with 312 patients with small-number LNM. Compared with the patients with small-number LNM, those with large-number LNM had larger tumors (13.0 ± 7.7 vs. 6.8 ± 3.8 mm, p < 0.001), and higher frequencies of multifocal nodules (42.3% vs. 22.4%, p < 0.001), taller-than-wide shape (82.1% vs. 56.7%, p < 0.001), calcifications (76.9% vs. 47.4%, p < 0.001), microcalcifications (68.0% vs. 36.5%, p < 0.001), capsule invasion (32.1% vs. 17.6%, p = 0.005), and ultrasound diagnosis of LNM (44.9% vs. 9.3%, p < 0.001). The multivariable analysis showed that nodule size (OR = 1.19, 95%CI: 1.11-1.27, p < 0.001), multifocal disease (OR = 2.50, 95%CI: 1.30-4.80, p = 0.006), taller-than-wide shape (OR = 0.45, 95%CI: 0.22-0.93, p = 0.032), and ultrasound diagnosis of LNM (OR = 5.57, 95%CI: 2.73-11.37, p < 0.001) were independently associated with large-number LNM. A nomogram was built, and the area under the receiver operating characteristics curve was 0.86 (95%CI: 0.81-0.90). A nomogram was successfully built to predict large-number LNM in patients with PTC, based on nodule size, multifocality, taller-than-wide shape, and ultrasound diagnosis of LNM.

The Application of Ultrasound Image-Based Radiomics in the Diagnosis of Mild Carpal Tunnel Syndrome.

OBJECTIVES: The ultrasound diagnosis of mild carpal tunnel syndrome (CTS) is challenging. Radiomics can identify image information that the human eye cannot recognize. The purpose of our study was to explore the value of ultrasound image-based radiomics in the diagnosis of mild CTS. METHODS: This retrospective study included 126 wrists in the CTS group and 88 wrists in the control group. The radiomics features were extracted from the cross-sectional ultrasound images at the entrance of median nerve carpal tunnel, and the modeling was based on robust features. Two radiologists with different experiences diagnosed CTS according to two guidelines. The area under receiver (AUC) operating characteristic curve, sensitivity, specificity, and accuracy were used to evaluate the diagnostic efficacy of the two radiologists and the radiomics model. RESULTS: According to guideline one, the AUC values of the two radiologists for CTS were 0.72 and 0.67, respectively; according to guideline two, the AUC were 0.73 and 0.68, respectively. The radiomics model achieved the best accuracy when 16 important robust features were selected. The AUC values of training set and test set were 0.92 and 0.90, respectively. CONCLUSIONS: The radiomics label based on ultrasound images had excellent diagnostic efficacy for mild CTS. It is expected to help radiologists to identify early CTS patients as soon as possible, especially for inexperienced doctors.

Application of ultrasound images-based radiomics in carpal tunnel syndrome: Without measuring the median nerve cross-sectional area.

PURPOSE: By constructing a prediction model of carpal tunnel syndrome (CTS) based on ultrasound images, it can automatically and accurately diagnose CTS without measuring the median nerve cross-sectional area (CSA). METHODS: A total of 268 wrists ultrasound images of 101 patients diagnosed with CTS and 76 controls in Ningbo NO.2 Hospital from December 2021 to August 2022 were retrospectively analyzed. The radiomics method was used to construct the Logistic model through the steps of feature extraction, feature screening, reduction, and modeling. The area under the receiver operating characteristic curve was calculated to evaluate the performance of the model, and the diagnostic efficiency of the radiomics model was compared with two radiologists with different experience. RESULTS: The 134 wrists in the CTS group included 65 mild CTS, 42 moderate CTS, and 17 severe CTS. In the CTS group, 28 wrists median nerve CSA were less than the cut-off value, 17 wrists were missed by Dr. A, 26 wrists by Dr. B, and only 6 wrists were missed by radiomics model. A total of 335 radiomics features were extracted from each MN, of which 10 features were significantly different between compressed and normal nerves, and were used to construct the model. The area under curve (AUC) value, sensitivity, specificity, and accuracy of the radiomics model in the training set and testing set were 0.939, 86.17%, 87.10%, 86.63%, and 0.891, 87.50%, 80.49%, and 83.95%, respectively. The AUC value, sensitivity, specificity, and accuracy of the two doctors in the diagnosis of CTS were 0.746, 75.37%, 73.88%, 74.63% and 0.679, 68.66%, 67.16%, and 67.91%, respectively. The radiomics model was superior to the two-radiologist diagnosis, especially when there was no significant change in CSA. CONCLUSION: Radiomics based on ultrasound images can quantitatively analyze the subtle changes in the median nerve, and can automatically and accurately diagnose CTS without measuring CSA, especially when there was no significant change in CSA, which was better than radiologists.

The value of radiomics model based on ultrasound image features in the differentiation between minimal breast cancer and small benign breast masses.

BACKGROUND: Female breast cancer has surpassed lung cancer as the most common cancer, and is also the main cause of cancer death for women worldwide. Breast cancer <1 cm showed excellent survival rate. However, the diagnosis of minimal breast cancer (MBC) is challenging. OBJECTIVE: The purpose of our research is to develop and validate an radiomics model based on ultrasound images for early recognition of MBC. METHODS: 302 breast masses with a diameter of <10 mm were retrospectively studied, including 159 benign and 143 malignant breast masses. The radiomics features were extracted from the gray-scale ultrasound image of the largest face of each breast mass. The maximum relevance minimum reduncancy and recursive feature elimination methods were used to screen. Finally, 10 features with the most discriminating value were selected for modeling. The random forest was used to establish the prediction model, and the rad-score of each mass was calculated. In order to evaluate the effectiveness of the model, we calculated and compared the area under the curve (AUC) value, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the model and three groups with different experience in predicting small breast masses, and drew calibration curves and decision curves to test the stability and consistency of the model. RESULTS: When we selected 10 radiomics features to calculate the rad-score, the prediction efficiency was the best, the AUC values for the training set and testing set were 0.840 and 0.793, which was significantly better than the insufficient experience group (AUC = 0.673), slightly better than the moderate experience group (AUC = 0.768), and was inferior to the experienced group (AUC = 0.877). The calibration curve and decision curve also showed that the radiomics model had satisfied stability and clinical application value. CONCLUSION: The radiomics model based on ultrasound image features has a satisfied predictive ability for small breast masses, and is expected to become a potential tool for the diagnosis of MBC, and it is a zero cost (in terms of patient participation and imaging time).

Ultrasound-based radiomics in the diagnosis of carpal tunnel syndrome: The influence of regions of interest delineation method on mode.

BACKGROUND: In the recent years, artificial intelligence (AI) algorithms have been used to accurately diagnose musculoskeletal diseases. However, it is not known whether the particular regions of interest (ROI) delineation method would affect the performance of the AI algorithm. PURPOSE: The purpose of this study was to investigate the influence of ROI delineation methods on model performance and observer consistency. METHODS: In this retrospective analysis, ultrasound (US) measures of median nerves affected with carpal tunnel syndrome (CTS) were compared to median nerves in a control group without CTS. Two methods were used for delineation of the ROI: (1) the ROI along the hyperechoic medial edge of the median nerve but not including the epineurium (MN) (ROI1); and (2) the ROI including the hyperechoic epineurium (ROI2), respectively. The intra group correlation coefficient (ICC) was used to compare the observer consistency of ROI features (i.e. the corresponding radiomics parameters). Parameters α1 and α2 were obtained based on the ICC of ROI1 features and ROI2 features. The ROC analysis was used to determine the area under the curve (AUC) and evaluate the performance of the radiologists and network. In addition, four indices, namely sensitivity, specificity, positive prediction and negative prediction were analyzed too. RESULTS: A total of 136 wrists of 77 CTS group and 136 wrists of 74 control group were included in the study. Control group was matched to CTS group according to the age and sex. The observer consistency of ROI features delineated by the two schemes was different, and the consistency of ROI1 features was higher (α1 Ëƒ α2). The intra-observer consistency was higher than the inter-observer consistency regardless of the scheme, and the intra-observer consistency was higher when chose scheme one. The performances of models based on the two ROI features were different, although the AUC of each model was greater than 0.8.The model performed better when the MN epineurium was included in the ROI. Among five artificial intelligence algorithms, the Forest models (model1 achieved an AUC of 0.921 in training datasets and 0.830 in testing datasets; model2 achieved an AUC of 0.967 in training datasets and 0.872 in testing datasets.) obtained the highest performance, followed by the support vector machine (SVM) models and the Logistic models. The performances of the models were significantly better than the inexperienced radiologist (Dr. B. Z. achieved an AUC of 0.702). CONCLUSION: Different ROI delineation methods may affect the performance of the model and the consistency of observers. Model performance was better when the ROI contained the MN epineurium, and observer consistency was higher when the ROI was delineated along the hyperechoic medial border of the MN.

Clinical analysis of ultrasound-guided radiofrequency ablation for recurrent colorectal liver metastases after hepatectomy.

BACKGROUND: RFA is designed to produce localized tumor destruction by heating the tumor and surrounding liver tissue, especially suitable for patients who do not qualify for hepatic resection. Many studies have reported that RFA was inferior to hepatectomy in the treatment of recurrent colorectal liver metastases. However, strong evidence is lacking in the literature. This study aimed to investigate the effect and clinical outcome of percutaneous ultrasound-guided RFA and repeat hepatic resection for recurrent colorectal liver metastases after hepatectomy. METHODS: From January 2007 to January 2014, 194 patients with recurrent colorectal liver metastases after hepatectomy diagnosed in our hospital was performed, and then divided into two groups based on different regimens: repeat hepatic resection group and RFA group. The clinical data of the two groups were analyzed. After treatment, the liver function-related indexes, complication rate, survival rate, and tumor recurrence of the two groups were recorded. The difference in short-term and long-term effects between repeat hepatic resection and RFA was identified by propensity score analysis. RESULTS: The number of metastases and the proportion of left and right lobe involved by tumor and preoperative chemotherapy in the RFA group were higher than those in the repeat hepatic resection group. The clinical data showed no significant difference between the two groups after using propensity score analysis. Compared with the RFA group, the liver function of the repeat hepatic resection group was significantly improved. After adjustment for potential confounders, no significant difference in liver function-related indexes was found between RFA and repeat hepatic resection, and the incidence of complications in the RFA group was lower. In survival analysis, there was no significant difference in OS and DFS between the two groups. CONCLUSIONS: RFA is a safe and effective therapeutic option for patients with recurrent colorectal liver metastases after hepatectomy.

Application of Machine-learning based on Radiomics Features in Differential Diagnosis of Superficial Lymphadenopathy.

OBJECTIVE: The accurate diagnosis of superficial lymphadenopathy is challenging. We aim to explore a non-invasive and accurate machine-learning method for distinguishing benign lymph nodes, lymphoma, and metastatic lymph nodes. METHODS: The clinical data and ultrasound images of 160 patients with superficial lymphadenopathy (58 benign lymph nodes, 62 lymphoma, 40 metastatic lymph nodes) admitted to our hospital from January 2020 to November 2022 were retrospectively studied. Patients were randomly divided into a training set and test set according to the ratio of 6:4. Firstly, the radiomics features of each lymph node were extracted, and then a series of statistical methods were used to avoid over-fitting. Then, the gradient boosting machine(GBM) was used to build the model. The area under receiver(AUC) operating characteristic curve, precision, recall rate and F1 value were calculated to evaluate the effectiveness of the model. RESULTS: Ten robust features were selected to build the model. The AUC values of benign lymph nodes, lymphoma and metastatic lymph nodes in the training set were 1.00, 0.98 and 0.99, and the AUC values of the test set were 0.96, 0.84 and 0.90, respectively. CONCLUSION: It was a reliable and non-invasive method for the differential diagnosis of lymphadenopathy based on the model constructed by machine learning.

The application of computer-aided diagnosis in Breast Imaging Reporting and Data System ultrasound training for residents-a randomized controlled study.

Background: The consistency of Breast Imaging Reporting and Data System (BI-RADS) classification among experienced radiologists is different, which is difficult for inexperienced radiologists to master. This study aims to explore the value of computer-aided diagnosis (CAD) (AI-SONIC breast automatic detection system) in the BI-RADS training for residents. Methods: A total of 12 residents who participated in the first year and the second year of standardized resident training in Ningbo No. 2 Hospital from May 2020 to May 2021 were randomly divided into 3 groups (Group 1, Group 2, Group 3) for BI-RADS training. They were asked to complete 2 tests and questionnaires at the beginning and end of the training. After the first test, the educational materials were given to the residents and reviewed during the breast imaging training month. Group 1 studied independently, Group 2 studied with CAD, and Group 3 was taught face-to-face by experts. The test scores and ultrasonographic descriptors of the residents were evaluated and compared with those of the radiology specialists. The trainees' confidence and recognition degree of CAD were investigated by questionnaire. Results: There was no statistical significance in the scores of residents in the first test among the 3 groups (P=0.637). After training and learning, the scores of all 3 groups of residents were improved in the second test (P=0.006). Group 2 (52±7.30) and Group 3 (54±5.16) scored significantly higher than Group 1 (38±3.65). The consistency of ultrasonographic descriptors and final assessments between the residents and senior radiologists were improved (κ3 > κ2 > κ1), with κ2 and κ3 >0.4 (moderately consistent with experts), and κ1 =0.225 (fairly agreed with experts). The results of the questionnaire showed that the trainees had increased confidence in BI-RADS classification, especially Group 2 (1.5 to 3.5) and Group 3 (1.25 to 3.75). All trainees agreed that CAD was helpful for BI-RADS learning (Likert scale score: 4.75 out of 5) and were willing to use CAD as an aid (4.5, max. 5). Conclusions: The AI-SONIC breast automatic detection system can help residents to quickly master BI-RADS, improve the consistency between residents and experts, and help to improve the confidence of residents in the classification of BI-RADS, which may have potential value in the BI-RADS training for radiology residents. Trial Registration: Chinese Clinical Trial Registry (ChiCTR2400081672).

Ultrasound radiomics nomogram for predicting large-number cervical lymph node metastasis in papillary thyroid carcinoma.

Purpose: To evaluate the value of preoperative ultrasound (US) radiomics nomogram of primary papillary thyroid carcinoma (PTC) for predicting large-number cervical lymph node metastasis (CLNM). Materials and methods: A retrospective study was conducted to collect the clinical and ultrasonic data of primary PTC. 645 patients were randomly divided into training and testing datasets according to the proportion of 7:3. Minimum redundancy-maximum relevance (mRMR) and least absolution shrinkage and selection operator (LASSO) were used to select features and establish radiomics signature. Multivariate logistic regression was used to establish a US radiomics nomogram containing radiomics signature and selected clinical characteristics. The efficiency of the nomogram was evaluated by the receiver operating characteristic (ROC) curve and calibration curve, and the clinical application value was assessed by decision curve analysis (DCA). Testing dataset was used to validate the model. Results: TG level, tumor size, aspect ratio, and radiomics signature were significantly correlated with large-number CLNM (all P< 0.05). The ROC curve and calibration curve of the US radiomics nomogram showed good predictive efficiency. In the training dataset, the AUC, accuracy, sensitivity, and specificity were 0.935, 0.897, 0.956, and 0.837, respectively, and in the testing dataset, the AUC, accuracy, sensitivity, and specificity were 0.782, 0.910, 0.533 and 0.943 respectively. DCA showed that the nomogram had some clinical benefits in predicting large-number CLNM. Conclusion: We have developed an easy-to-use and non-invasive US radiomics nomogram for predicting large-number CLNM with PTC, which combines radiomics signature and clinical risk factors. The nomogram has good predictive efficiency and potential clinical application value.

A nomogram based on ultrasound radiomics for predicting the invasiveness of cN0 single papillary thyroid microcarcinoma.

Background: Up to 15.3% of papillary thyroid microcarcinoma (PTMC) patients with negative clinical lymph node metastasis (cN0) were confirmed to have pathological lymph node metastasis in level VI. Conventional ultrasound (US) focuses on the characteristics of tumor capsule and the periphery to determine whether the tumor has invasive growth. However, due to its small size, the typical features of invasiveness shown by conventional 2-dimensional (2D) US are not well visualized. US-based radiomics makes use of artificial intelligence and big data to build a model that can help improving diagnostic accuracy and providing prognostic implication of the disease. We hope to establish and assess the value of a nomogram based on US radiomics combined with independent risk factors in predicting the invasiveness of a single PTMC without clinical lymph node metastasis (cN0). Methods: A total of 317 patients with cN0 single PTMC who underwent US examination and operation were included in this retrospective cohort study. Patients were randomly divided into training and testing set in the ratio of 8:2. The US images of all patients were segmented, and the radiomics features were extracted. In the training dataset, the US with features of minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) were selected and radiomics signatures were then established according to their respective weighting coefficients. Univariate and multivariate logistic regression analyses were employed to generate the risk factors of possible invasive PTMC. The nomogram is then made by combining high risk factors and the radiomics signature. The efficiency of the nomogram was evaluated by the receiver operating characteristic (ROC) curve and calibration curve, and its clinical application value was assessed by decision curve analysis (DCA). The testing dataset was used to validate the model. Results: In the model, seven radiomics features were selected to establish the radiomics signature. A nomogram was made by incorporating clinically independent risk factors and the radiomics signature. Both the ROC curve and calibration curve showed good prediction efficiency. The area under the curve (AUC), accuracy, sensitivity, and specificity of the nomogram in the training data were 0.76 [95% confidence interval (CI): 0.71-0.82], 0.811, 0.914, and 0.727, respectively whereas the results of the testing dataset were 0.71 (95% CI: 0.58-0.84), 0.841, 0.533, and 0.868. As such, the efficacy of the nomogram in predicting the invasiveness of PTMC was subsequently validated by the DCA. Conclusions: Nomogram based on thyroid US radiomics has an excellent predictive value of the potential invasiveness of a single PTMC without clinical lymph node metastasis. With these promising results, it can potentially be the imaging marker used in daily clinical practice.

Suppression of the transforming growth factor-ß signaling pathway produces a synergistic effect of combination therapy with programmed death receptor 1 blockade and radiofrequency ablation against hepatic carcinoma in mice.

Primary liver cancer (PLC) significantly affects the health of patients globally owing to its high morbidity and low survival rate. Radiofrequency ablation (RFA) has recently been introduced for the clinical treatment of PLC. However, significant immunosuppressive effects are induced by RFA, which limits its application. This study aimed to explore the potential of combination therapy with RFA by investigating the effects of siRNAs against programmed death receptor 1 (PD-1) and transforming growth factor-ß (TGF-ß) on the antitumor effect induced by RFA. We observed that compared with si-NC, cell viability was reduced, apoptosis rate was elevated, release of inflammatory factors and percentage of CD3+CD8+ cells were increased, and the PI3K/AKT/mTOR pathway was repressed in the co-culture of RFA-treated H22 cells and CD8+ T cells by transfection with si-PD-1 and si-TGF-ß; these effects were further enhanced by co-transfection with si-PD-1 and si-TGF-ß. Additionally, in H22 cell xenograft-bearing mice treated with RFA, compared with the si-NC group, repressed tumor growth, prolonged survival, increased production of inflammatory factors and expression of CD3 and CD8 in tumor tissues, and downregulation of the PI3K/AKT/mTOR pathway were observed in the si-PD-1 and si-TGF-ß groups; these effects were further enhanced in the si-PD-1 + si-TGF-ß group. Taken together, our data revealed that suppression of the TGF-ß signaling pathway produced a synergistic antitumor effect of combination therapy with PD-1 blockade and RFA against PLC. [Figure: see text].

Diagnostic value of artificial intelligence automatic detection systems for breast BI-RADS 4 nodules.

BACKGROUND: The incidence rate of breast cancer has exceeded that of lung cancer, and it has become the most malignant type of cancer in the world. BI-RADS 4 breast nodules have a wide range of malignant risks and are associated with challenging clinical decision-making. AIM: To explore the diagnostic value of artificial intelligence (AI) automatic detection systems for BI-RADS 4 breast nodules and to assess whether conventional ultrasound BI-RADS classification with AI automatic detection systems can reduce the probability of BI-RADS 4 biopsy. METHODS: A total of 107 BI-RADS breast nodules confirmed by pathology were selected between June 2019 and July 2020 at Hwa Mei Hospital, University of Chinese Academy of Sciences. These nodules were classified by ultrasound doctors and the AI-SONIC breast system. The diagnostic values of conventional ultrasound, the AI automatic detection system, conventional ultrasound combined with the AI automatic detection system and adjusted BI-RADS classification diagnosis were statistically analyzed. RESULTS: Among the 107 breast nodules, 61 were benign (57.01%), and 46 were malignant (42.99%). The pathology results were considered the gold standard; furthermore, the sensitivity, specificity, accuracy, Youden index, and positive and negative predictive values were 84.78%, 67.21%, 74.77%, 0.5199, 66.10% and 85.42% for conventional ultrasound BI-RADS classification diagnosis, 86.96%, 75.41%, 80.37%, 0.6237, 72.73%, and 88.46% for automatic AI detection, 80.43%, 90.16%, 85.98%, 0.7059, 86.05%, and 85.94% for conventional ultrasound BI-RADS classification with automatic AI detection and 93.48%, 67.21%, 78.50%, 0.6069, 68.25%, and 93.18% for adjusted BI-RADS classification, respectively. The biopsy rate, cancer detection rate and malignancy risk were 100%, 42.99% and 0% and 67.29%, 61.11%, and 1.87% before and after BI-RADS adjustment, respectively. CONCLUSION: Automatic AI detection has high accuracy in determining benign and malignant BI-RADS 4 breast nodules. Conventional ultrasound BI-RADS classification combined with AI automatic detection can reduce the biopsy rate of BI-RADS 4 breast nodules.

Ultrasound-guided secondary radiofrequency ablation combined with chemotherapy in gastric cancer with recurrent liver metastasis.

BACKGROUND: The incidence and mortality of gastric cancer are in the second and third place of malignant tumor in China, respectively. Liver metastasis is an important cause of death of these patients. This study is to explore whether the secondary radiofrequency ablation (RFA) treatment can prolong the survival period and improve the life quality of patients with gastric cancer and recurrent liver metastases. METHODS: A total of 87 patients with gastric cancer and recurrent liver metastases were retrospective analyzed, 46 cases were assigned into study group and 41 cases in control group. The efficacy of the two groups was observed, and the prognostic factors were analyzed. RESULTS: The median survival time in the study group was significantly longer than that in the control group (P<0.05). The survival rate of the study group was significantly higher than that of the control group (both P<0.05). The life quality scores of the study group were significantly higher than the control group (both P<0.05). CONCLUSIONS: Ultrasound-mediated secondary RFA combined with chemotherapy is superior to chemotherapy alone in the treatment of gastric cancer with recurrent liver metastases.

Differential Diagnosis of Benign and Malignant Thyroid Nodules Using Deep Learning Radiomics of Thyroid Ultrasound Images.

PURPOSE: We aimed to propose a highly automatic and objective model named deep learning Radiomics of thyroid (DLRT) for the differential diagnosis of benign and malignant thyroid nodules from ultrasound (US) images. METHODS: We retrospectively enrolled and finally include US images and fine-needle aspiration biopsies from 1734 patients with 1750 thyroid nodules. A basic convolutional neural network (CNN) model, a transfer learning (TL) model, and a newly designed model named deep learning Radiomics of thyroid (DLRT) were used for the investigation. Their diagnostic accuracy was further compared with human observers (one senior and one junior US radiologist). Moreover, the robustness of DLRT over different US instruments was also validated. Analysis of receiver operating characteristic (ROC) curves were performed to calculate optimal area under it (AUC) for benign and malignant nodules. One observer helped to delineate the nodules. RESULTS: AUCs of DLRT were 0.96 (95% confidence interval [CI]: 0.94-0.98), 0.95 (95% confidence interval [CI]: 0.93-0.97) and 0.97 (95% confidence interval [CI]: 0.95-0.99) in the training, internal and external validation cohort, respectively, which were significantly better than other deep learning models (P < 0.01) and human observers (P < 0.001). No significant difference was found when applying DLRT on thyroid US images acquired from different US instruments. CONCLUSIONS: DLRT shows the best overall performance comparing with other deep learning models and human observers. It holds great promise for improving the differential diagnosis of benign and malignant thyroid nodules.

Drug-eluting beads transarterial chemoembolization sequentially combined with radiofrequency ablation in the treatment of untreated and recurrent hepatocellular carcinoma.

BACKGROUND: Drug-eluting beads transarterial chemoem-bolization (DEB-TACE) has the advantages of slow and steady release, high local concentration, and low incidence of adverse drug reactions compared to the traditional TACE. DEB-TACE combined with sequentially ultrasound-guided radiofrequency ablation (RFA) therapy has strong anti-cancer effects and little side effects, but there are fewer related long-term studies until now. AIM: To explore the outcome of DEB-TACE sequentially combined with RFA for patients with primary hepatocellular carcinoma (HCC). METHODS: Seventy-six patients with primary HCC who underwent DEB-TACE sequentially combined with RFA were recruited. Forty patients with untreated HCC were included in Group A, and 36 patients with recurrent HCC were included in Group B. In addition, 40 patients with untreated HCC who were treated with hepatectomy were included in Group C. The serological examination, preoperative magnetic resonance imaging examination, and post-treatment computed tomography enhanced examination were performed for all patients. The efficacy was graded as complete remission (CR), partial remission (PR), stable disease and progressive disease at the 3rd, 6th, and 9th. All patients were followed up for 3 years and their overall survival (OS), disease-free survival (DFS) were assessed. RESULTS: The efficacy of Group A and Group C was similar (P > 0.05), but the alanine aminotransferase, aspartate aminotransferase and total bilirubin of Group A were lower than those of Group C (all P < 0.05). The proportions of CR (32.5%), PR (37.5%) were slightly higher than Group A (CR: 27.5%, PR: 35%), but the difference was not statistically significant (χ 2 = 0.701, P = 0.873). No operational-related deaths occurred in Group A and Group C. The OS (97.5%, 84.7%, and 66.1%) and the DFS (75.0%, 51.7%, and 35.4%) of Group A at the 1st, 2nd, and 3rd year after treatment were similar with those of Group C (OS: 90.0%, 79.7%, and 63.8%; DFS: 80.0%, 59.7%, and 48.6%; P > 0.05). The OS rates in Group A and Group B (90%, 82.3%, and 66.4%) were similar (P > 0.05). The DFS rates in Group B (50%, 31.6%, and 17.2%) were lower than that of Group A (P = 0.013). CONCLUSION: The efficacy of DEA-TACE combined with RFA for untreated HCC is similar with hepatectomy. Patients with recurrent HCC could get a longer survival time through the combined treatment.

Effect of STAT3 decoy oligodeoxynucleotides mediated by ultrasound-targeted microbubbles combined with ultrasound on the growth of squamous cell carcinoma of the esophagus.

Effect of STAT3 decoy oligodeoxynucleotides (ODN) transduced by ultrasound microbubbles combined with ultrasound on the growth of esophageal squamous cell carcinoma and its mechanism were analyzed. EC9706 cells were cultured in vitro and divided into four groups: group E (ultrasound microbubble + ultrasound irradiation), group P (liposome + ultrasound irradiation), group C (ultrasound), and group CC (ultrasound microbubbles). Mutant ODNs were used in groups E and P and the control group was group EC and PC, respectively. Immunofluorescence assay and flow cytometry were used to detect the transfection efficiency of each group. MTT colorimetric assay was performed to analyze the inhibition rate in each group. The effect of STAT3 decoy ODN on the proliferation of esophageal squamous carcinoma cells was calculated. Revese transcription-quantitative PCR (RT-qPCR) and western blotting were performed to detect the expression of the STAT signaling pathway downstream of gene expression levels. The model of subcutaneous transplantation of nude mice was used to show the effect of different transfections and STAT3 decoy ODN on the weight and volume of the transplanted tumor in mice. The cell inhibition rate was higher in group E than in groups P (F=8.382, P<0.001) and CC (F=6.469, P<0.001). Compared with groups EC, PC and C, respectively, the mRNA expression of STAT3, bcl-xL and Cyclin D1 decreased in groups E, P and CC (F=5.328, P<0.001). The weight and volume of nude mice in groups E, P and CC exhibited an inhibitory effect on the weight and volume of nude mice. Ultrasound irradiation combined with ultrasound microbubbles is an effective transfection method. The transfection of STAT3 decoy ODN can significantly inhibit the activity of esophageal squamous cell carcinoma cells and enhance apoptosis of cells, which has potential clinical value.

Contrast-enhanced ultrasound is better than magnetic resonance imaging in evaluating the short-term results of microwave ablation treatment of uterine fibroids.

Contrast-enhanced ultrasound (CEUS) and magnetic resonance imaging (MRI) were compared in evaluating the short-term effects of microwave ablation (MWA) on uterine fibroids. A total of 60 patients with uterine fibroids treated by MWA were enrolled in the experimental group during their two-year follow-up period according to the inclusion criteria. Conventional two-dimensional US, MRI and CEUS were performed to determine the volume reduction and the fibroid residue by displaying the size, echo and signal intensity of fibroids prior to and after MWA treatment. As the control group, 60 consecutive patients were recruited on their follow-up visit at least two years after MWA treatment of uterine fibroids. Significant differences were observed in the wash-in rate (WiR) of the fibroid tissue, start time difference, rise time ratio (RTR) and WiR ratio between the experimental and control groups (P<0.05). However, the WiR of fibroid vessel, total area under the curve of fibroid vessel and tissue, and rise time difference (RTD) between fibroid vessel and tissue did not display any significant differences between the two groups. Fibroids were either reduced in volume or cured by MWA therapy in patients with uterine fibroids. The reductions in volume of hypointense, isointense and hyperintense fibroids were 62.42±18.13, 53.27±10.05 and 47.43±9.56%, respectively, on T1-weighted imaging (T1WI). On T2WI, the corresponding reductions were 67.32±32.63, 59.36±19.36 and 42.63±10.37%, respectively. The higher the signal intensity on T1WI and T2WI, the lower the reduction in volume. It is indicative that different blood supply to fibroids results in different ablation. CEUS was proved to be more effective than MRI in evaluating the effects of MWA on uterine fibroids during the first postoperative year.

Clinical value of liver and spleen shear wave velocity in predicting the prognosis of patients with portal hypertension.

AIM: To explore the relationship of liver and spleen shear wave velocity in patients with liver cirrhosis combined with portal hypertension, and assess the value of liver and spleen shear wave velocity in predicting the prognosis of patients with portal hypertension. METHODS: All 67 patients with liver cirrhosis diagnosed as portal hypertension by hepatic venous pressure gradient in our hospital from June 2014 to December 2014 were enrolled into this study. The baseline information of these patients was recorded. Furthermore, 67 patients were followed-up at 20 mo after treatment, and liver and spleen shear wave velocity were measured by acoustic radiation force impulse at the 1st week, 3rd month and 9th month after treatment. Patients with favorable prognosis were assigned into the favorable prognosis group, while patients with unfavorable prognosis were assigned into the unfavorable prognosis group. The variation and difference in liver and spleen shear wave velocity in these two groups were analyzed by repeated measurement analysis of variance. Meanwhile, in order to evaluate the effect of liver and spleen shear wave velocity on the prognosis of patients with portal hypertension, Cox's proportional hazard regression model analysis was applied. The ability of those factors in predicting the prognosis of patients with portal hypertension was calculated through receiver operating characteristic (ROC) curves. RESULTS: The liver and spleen shear wave velocity in the favorable prognosis group revealed a clear decline, while those in the unfavorable prognosis group revealed an increasing tendency at different time points. Furthermore, liver and spleen shear wave velocity was higher in the unfavorable prognosis group, compared with the favorable prognosis group; the differences were statistically significant (P < 0.05). The prognosis of patients with portal hypertension was significantly affected by spleen hardness at the 3rd month after treatment [relative risk (RR) = 3.481]. At the 9th month after treatment, the prognosis was affected by liver hardness (RR = 5.241) and spleen hardness (RR = 7.829). The differences between these two groups were statistically significant (P < 0.05). The ROC analysis revealed that the area under the curve (AUC) of spleen hardness at the 3rd month after treatment was 0.644, while the AUCs of liver and spleen hardness at the 9th month were 0.579 and 0.776, respectively. These might predict the prognosis of patients with portal hypertension. CONCLUSION: Spleen hardness at the 3rd month and liver and spleen shear wave velocity at the 9th month may be used to assess the prognosis of patients with portal hypertension. This is hoped to be used as an indicator of predicting the prognosis of patients with portal hypertension.

Serum concentrations of matrix metalloproteinase-9 and vascular endothelial growth factor affect the prognosis of primary hepatic carcinoma patients treated with percutaneous ethanol injection.

AIMS: The present study is to investigate changes in serum concentrations of matrix metalloproteinase-9 (MMP-9) and vascular endothelial growth factor (VEGF) before and after percutaneous ethanol injection (PEI) in primary hepatic carcinomas (PHC), and their effects on the prognosis. METHODS: A total of 100 patients with PHC received PEI treatment in our hospital between July 2010 and July 2014. Another 100 PHC patients who had PHC resected were included as control group. For PEI treatment, anhydrous ethanol was slowly injected into the tumor every 2-3 days for consecutive 4-10 times. The evaluation of treatment efficacy was performed in accordance with the standards by Union for International Cancer Control. Serum concentrations of MMP-9 and VEGF were determined using enzyme-linked immunosorbent assay. The median values of MMP-9 and VEGF concentrations were used as the cutoff value to discriminate high and low MMP-9 and VEGF contents. Kaplan-Meier plots were used to examine how serum concentrations of MMP-9 and VEGF affected postoperative survival of PHC patients. RESULTS: PEI treatment decreased the serum contents of MMP-9 and VEGF after the surgery. PEI had high effectiveness against PHC tumors during the surgery. PEI treatment led to higher survival rate in PHC patients compared with PHC resection. Serum levels of MMP-9 and VEGF were related to different Child grading, Kps scoring, BCLC staging and AFP contents. Lower preoperative serum concentrations of MMP-9 and VEGF might lead to longer survival time of PHC patients after PEI. CONCLUSIONS: PEI treatment alters serum concentrations of MMP-9 and VEGF in PHC patients, which may have great effect on the prognosis.