Objective: Based on magnetic resonance imaging of patients with brain metastases, using imaging omics methods to differentiate the primary lesion of brain metastases and determine the specific source of the patient’s brain metastases. Methods: The data of this study were collected from patients with brain metastasis who treated in Liaoning Cancer Hospital from January 2017 to September 2020. Experienced imaging doctors manually delineate the active area of the patient’s brain metastasis tumor, and obtain a 4 mm circular area containing the active area and the surrounding area through computer methods. And 1 967 imaging omics features were extracted from each region of the contrast-enhanced T1-weighted imaging (CE-T1WI) and T2-weighted imaging (T2WI) sequences. After merging the sequences, a three-step feature screening method was performed using U-test, least absolute shrinkage and selection operator (LASSO) logistic regression, and Akaike information criterion (AIC), draw the receive operating characteristic (ROC) curve to calculate the area under curve (AUC) value as an indicator of the classification performance of the discriminative model. Results: A total of 215 patients with brain metastases were identified, including 100 cases of lung origin, 50 cases of breast origin, 50 cases of gastrointestinal origin, and 15 cases of other origin. From each of the two sequences, 1 967 imaging histological features were extracted. Three radiomics models were successfully constructed to differentiate whether brain metastases arose from lung cancer lesions in the RS_Lung model, breast cancer lesions in the RS_breast model and gastrointestinal cancer lesions in the RS_Gastrointestic model, with training set AUC of 0.898, 0.872 and 0.938, sensitivity of 0.908, 0.744 and 0.860, specificity of 0.818, 0.879 and 0.909, respectively. Conclusion: This study achieved good results in the distinguishing task derived from brain metastases using an imaging omics model constructed based on a 4 mm annular region, which has the potential to be a noninvasive preoperative new marker and guide personalized treatment options for patients with brain metastases.

Objective: To investigate the value of multiple radiomics models constructed by combining clinical risk factors and multiparametric magnetic resonance imaging (MRI) radiomics features to predict microsatellite instability (MSI) in rectal cancer. Methods: A total of 149 rectal cancer patients were included in the First Affiliated Hospital of Bengbu Medical University from December 2020 to November 2023, including 34 patients with MSI and 115 patients with microsatellite stability (MSS). Based on MRI examination of multiple sequence images, 3D regions of interest were delineated, radiomics features were extracted and dimensionality was reduced to select the best features. Then, five different machine learning algorithms, including logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and naive Bayes tree (NBT) were used to construct different imaging radiomics models using the optimal radiomics features. And receiver operating characteristic (ROC) curves were drew to evaluate the diagnostic performance of different models. Results: The RF model showed the most stable performance, and the clinical imaging radiomics joint model nomogram based on clinical independent risk factors and imaging radiomics showed high diagnostic efficiency for MSI in rectal cancer. The area under curve (AUC) of the training group and the validation group were 0.923 and 0.914, respectively, indicating the most significant evaluation of MSI in rectal cancer. Conclusion: Combining different machine learning algorithms, a clinical imaging radiomics nomogram constructed from clinical risk factors and multi-parameter MRI radiomics features can effectively predict the unstable state of preoperative rectal cancer.

Objective: To investigate the feasibility of fusion registration of high-frequency ultrasound (HFUS)-pathology images and predictive value of model for predicting the pathological tissue components in in vitro models based on deep learning (DL) networks. Methods: Sixty in vitro mimetic tumor models containing four different biological tissues and localized particles were prepared. HFUS images and wide slide images (WSI) of the same slide were obtained under physical registration. The obtained images were quality controlled and selected, and the region of interest (ROI) was manually outlined along the edges of the tissues in the WSI and then transferred to HFUS images. The datasets were consisted of original images and corresponding ROIs and were divided into the training set (n=462), validation set (n=34) and testing set (n=38) at the ratio of 13∶1∶1. DL models were developed via transfer learning DeepLabV3, FCN-50 and MobileNetV3 networks. The pixel accuracy (PA), precision, recall and F1-score were used to quantify and compare the performance of each model in the training and testing datasets. The automatically segmented images were output. Results: The DL models based on DeepLabV3, FCN-50 and MobileNetV3 networks had high accuracy and similarity for automatically segmentation of different tissue components in the testing set, and the MobileNetV3 model outperformed others with the PA of 91.4% and F1-score of 87.1%. There was no significant difference between performance of models (all P> 0.05). There were statistically significant differences between the efficiencies of models for predicting different in vitro biological tissue components (all P<0.001), with the best of liver tissue. Conclusion: The constructed ultrasound-pathology fusion models in this study can effectively recognize the in vitro tissue components in ultrasound images and provide the methodological basis for further clinical applications.

Objective: To investigate the predictive value of a scoring model based on conventional ultrasound combined with clinicopathological features for pathologic complete response (pCR) in human epidermal growth factor receptor 2 (HER2) positive breast cancer after neoadjuvant chemotherapy (NAC). Methods: A retrospective analysis was performed in patients who confirmed as HER2 positive breast cancer by ultrasound-guided coarse needle biopsies and followed by eight cycles of NAC from January 2022 to August 2023 in The First Affiliated Hospital of Nanjing Medical University. According to the Miller-Payne grading system, the patients were divided into pathological complete response (pCR) group and non-pathological complete response (non-pCR) group. The differences of clinical and pathological data and ultrasound imaging features between the two groups were analyzed by t test or χ²/Fisher test. Multivariate regression analysis was used to determine the independent predictors of pCR in HER2 positive breast cancer, and a nomogram was established to visualize the predictive efficacy of related factors for pCR. Results: A total of 103 patients were included, 51 in the pCR group and 52 in the non-pCR group. Univariate analysis revealed significant differences between pCR and non-pCR groups regarding tumor molecular classification (χ²=12.266, P<0.001), as well as the ultrasound features including the rate of longest diameter change ΔD₂, ΔD₄, ΔD₆, ΔD₈ (t=-2.760, P=0.007; t=-2.557, P=0.012; t=-4.006, P<0.001; t=-2.872, P=0.005) and volume change rate ΔV₂, ΔV₄, ΔV₆, ΔV₈ (t=-4.167, P<0.001; Z=-3.443, P<0.001; Z=-4.518, P<0.001; Z=-3.708, P<0.001) along with resistance index (RI) measurements in the 4th, the 6th and the 8th cycle (Z=-2.108, P=0.035; Z=-2.386, P=0.017; Z=-3.708, P<0.001). Multivariate analysis showed that tumor molecular type (OR=0.15, 95% CI 0.03-0.64, P=0.005) and tumor volume change rate after the second cycle of NAC ΔV₂ (OR=121.47, 95% CI 4.25-3468.72, P=0.010) were independent predictors of pCR in HER2 positive breast cancer after NAC (P<0.05). The area under the receiver operating characteristic (ROC) curve (AUC) of the nomogram model was 0.82 (95% CI 0.74-0.91), the sensitivity was 85%, and the specificity was 75%. Calibration chart and decision curve analysis (DCA) showed it has good application value in clinical evaluation. Conclusion: The developed nomogram scoring model integrating ultrasound and clinicopathological features demonstrates strong predictive potential for identifying pCR among HER2 positive breast cancer patients post-NAC, thereby offering valuable insights into subsequent treatment decisions.

Objective: To explore the ultrasound imaging evaluation of breast cancer microcalcifications under the expression status of different SOX gene family transcription factors. Methods: The study selected breast cancer patients diagnosed through pathological examination in Xingtai Central Hospital from January 2020 to October 2023. All patients underwent surgical treatment. Breast cancer tissue was obtained from the tumor site, along with adjacent normal tissue at least 5 cm away from the cancerous tissue. The collected tissues were immediately preserved in liquid nitrogen at -196℃ for subsequent research. Preoperative ultrasound examinations, including routine ultrasound, shear wave elastography, and contrast-enhanced ultrasound were performed. Postoperative pathological histological testing was conducted to detect SOX4 expression, and the relationship between ultrasonographic microcalcifications and SOX4 protein expression was analyzed. Results: In 215 patients, the proportion of high SOX4 expression in breast cancer tissue was 86.05%, significantly higher than that in adjacent normal tissue, which was 48.84% (χ²=67.783, P=0.000). Breast cancer microcalcifications were closely associated with clinical pathological features such as tumor differentiation and lymph node metastasis (P<0.05). High SOX4 expression in breast cancer was closely correlated with microcalcifications, Alder blood flow grading, peripheral radial enhancement, and the rim sign (P<0.05). Logistic regression analysis showed that microcalcifications (OR=1.839, 95% CI 1.538-2.198), peripheral radial enhancement (OR=1.795, 95% CI 1.089-2.959), and rim sign (OR=1.496, 95% CI 1.007-2.223) were risk factors for high SOX4 expression in breast cancer (P<0.05). Conclusion: SOX4 is implicated as a pivotal contributor to the pathogenesis and progression of breast cancer. The manifestation of microcalcifications in ultrasound imaging exhibits a significant correlation with tumor differentiation and lymph node metastasis, underscoring microcalcification as a prognostic marker for elevated SOX4 expression. Clinically, the identification of microcalcification patterns serves as a predictive tool for the expression of SOX4 transcription factors, which is anticipated to augment the precision of early diagnostic modalities and inform the development of targeted therapeutic strategies in breast cancer management.

Objective: To explore the value of multimodal ultrasound imaging in predicting central lymph node metastasis of papillary carcinoma of the thyroid (PTC). Methods: Patients with PTC confirmed by operation and pathology were selected in The First People’s Hospital of Foshan from March 2022 to February 2024. The patients were examined by conventional ultrasound, shear wave elastography (SWE) and superb microvascular imaging (SMI) before surgery. Conventional ultrasound was used to evaluate lesion maximum diameter, location, shape, internal parenchymal echo, microcalcification, the ratio of longitudinal and transverse diameter, capsule continuity. SWE was used to measure the mean of Young’s modulus (E_mean). SMI was used to measure vascular index (VI). Single-factor and multi-factor logistic regression analysis were used to analyze the risk factors of the central lymph node metastasis in PTC patients. Results: A total of 110 patients included 52 patients with central lymph node metastasis (metastatic group) and 58 patients without central lymph node metastasis (non-metastatic group). There were significant differences in age, maximum diameter, shape, parenchymal echo, microcalcification, capsule continuity, E_mean and VI between the two groups (all P<0.05). Multi-factor logistic regression analysis showed that age, maximum diameter, parenchymal echo and E_mean were the independent risk factors of central lymph node metastasis (all P<0.05). Conclusion: Multimodal ultrasound has a certain value in predicting the central lymph node metastasis of PTC.

Objective: To evaluate and compare the diagnostic value of contrast-enhanced ultrasound (CEUS), ultrasound-guided fine-needle aspiration biopsy (US-FNAB), and the combination of the two in the diagnosis of Thyroid Imaging Reporting and Data System (TI-RADS) 4 nodules of the thyroid gland. Methods: Patients with TI-RADS 4 thyroid nodules having clear pathological findings from February 2019 to November 2023 in Tangshan Central Hospital and Tangshan Gongren Hospital were retrospectively analyzed. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of CEUS, US-FNAB, and the combination of the two methods were calculated by using postoperative pathology as a criterion, and the diagnostic efficacy of CEUS, US-FNAB and their combination was compared by drawing the receiver operating characteristic (ROC) curve. Results: In the differential diagnosis of seventy-five patients benign and malignant thyroid TI-RADS 4 nodules, the diagnostic sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of CEUS were 70.7%, 70.6%, 70.6%, 74.4%, and 66.7%, respectively, and of US-FNAB were 75.6%, 79.4%, 77.3%, 81.6% and 73.0%, respectively, and the combination of the two were 97.6%, 58.8%, 80.0%, 74.1%, and 95.2%, respectively. The false-positive rates of CEUS, US-FNAB, and the combination of the two were 29.3%, 24.4%, and 2.4%, respectively. The false-negative rates of CEUS, US-FNAB and the combination of the two were 29.4%, 20.6% and 41.2%, respectively. The AUC of CEUS, US-FNAB and the combination of the two applied diagnostics were 0.707, 0.775, and 0.782, respectively, as derived from plotting the ROC curve. Conclusion: Both CEUS and US-FNAB have a certain value in the differential diagnosis of benign and malignant thyroid TI-RADS 4 nodules, and their diagnostic value can be improved when they are combined, which can be combined when it is difficult to distinguish between benign and malignant in the clinical situation.

Objective: To investigate the diagnostic value of contrast-enhanced ultrasound (CEUS) guided fine-needle aspiration biopsy (FNAB) for papillary thyroid carcinoma of the thyroid (PTC) with a diameter of ≥10 mm. Methods: The data of patients with PTC (≥10 mm in diameter) confirmed by postoperative pathology from May 2020 to August 2023 in Minhang Hospital of Fudan University were reviewed and analyzed. All nodules were first examined by CEUS. After the CEUS, conventional ultrasound-guided FNAB (US-FNAB) was performed to the two-dimensional ultrasound image of the largest diameter of the nodule. Two tissue smears (labeled A₁ and A₂) were used as conventional puncture group. For the same nodule, CEUS images of the same nodule were observed, and the perfused area of the nodule was selected for target puncture, and 2 aspirated cell tissue smears (labeled B₁ and B₂) as the target puncture group. With the postoperative pathological findings as the gold standard, the diagnostic value of CEUS, US-FNAB and CEUS-FNAB for PTC was analyzed using the consistency Kappa test. Results: A total of 149 patients (155 nodes) were included in this study. The sensitivity, specificity and accuracy of CEUS in diagnosing 155 PTCs were 87.1%, 68.6% and 83.2%, respectively; the sensitivity, specificity and accuracy of US-FNAB were 92.9%, 78.0% and 85.6%; and the sensitivity, specificity and accuracy of CEUS-FNAB were 98.1%, 79.6% and 94.9%, respectively. With the postoperative pathology results as the gold standard, Kappa consistency test was performed between the CEUS results and the postoperative pathology results, with Kappa=0.393 (Kappa<0.4), poor consistency, and statistically significant difference (P<0.01); the Kappa value of the US-FNAB results=0.574 (0.75>Kappa≥0.4), the consistency was fair, and the difference was statistically significant (P<0.01); Kappa value of CEUS-FNAB results=0.773 (Kappa>0.75), consistency was good, and the difference was statistically significant (P<0.01). Conclusion: CEUS technology is helpful to detect suspicious lesions of thyroid cancer, and CEUS-FNAB can improve the accuracy of puncture and lesion detection rate, which is of great significance for the diagnosis of PTC.

Objective: To explore the application value of contrast-enhanced ultrasound in the diagnosis of Xp11.2 translocation /TFE3 gene fusion associated renal carcinoma (Xp11.2 translocation renal carcinoma). Methods: Patients with Xp11.2 translocation renal carcinoma confirmed by pathological examination were retrospectively enrolled as XP group between January 2021 and May 2023, while patients with clear cell renal cell carcinoma (ccRCC) during the same period were enrolled as ccRCC group. All patients underwent scans of routine ultrasound and contrast-enhanced ultrasound. The ultrasonographic findings in the two groups were compared. The diagnostic value of contrast-enhanced ultrasound in Xp11.2 translocation /TFE3 gene fusion-associated renal carcinoma was analyzed. Results: A total of 28 patients were included in the XP group and 40 in the ccRCC group. The age in XP group was younger than that in ccRCC group, and proportion of males was lower than that in ccRCC group (χ²=5.419; χ²=7.502, P<0.05). Routine ultrasound showed that there were significant differences in boundary and calcification between XP group and ccRCC group (P<0.05). Contrast-enhanced ultrasound showed that there were significant differences in perfusion mode and peak enhancement mode between XP group and ccRCC group (P<0.05). The time to peak (TTP) and begin to fade (BTF) in XP group were shorter than those in ccRCC group (P<0.05). There was no significant difference in arrival time (AT) between the two groups (P>0.05). Receiver operating characteristic (ROC) curve showed that AUC of TTP combined with BTF in the diagnosis of Xp11.2 translocation renal carcinoma was 0.855, greater than that of single index (0.773, 0.744; Z=2.202; Z=2.141, P<0.05). Binary logistic analysis showed that calcification, peak low enhancement or isoenhancement, TTP≤21.15 s, BTF≤25.31 s were all independent risk factors for the diagnosis of Xp11.2 translocative renal carcinoma by contrast ultrasound (P<0.05). Conclusion: Quantitative parameters of contrast-enhanced ultrasound have good differential diagnosis effect and clinical application value in Xp11.2 translocation renal carcinoma.

Objective: To explore the performance of high-frequency ultrasound after manipulation under anesthesia (MUA) of knee joint. Methods: The clinical and ultrasound imaging data of 35 patients who underwent MUA of knee joint in Shanghai Fengcheng hospital from January 2021 to June 2022 were retrospectively analyzed. The ultrasound parameters of each patient included joint effusion, synovium, quadriceps tendon and patellar ligament echo and thickness before and after the MUA. Results: After 2 weeks of MUA, 37.1% of the patients had signs of synovitis, and 20.0% of the patients had joint effusion. Echo reduction was observed in all the quadriceps tendon and patellar ligament, and the thickness were increased with an average of 23.2% and 18.3%, respectively. Macro-calcification was found in 28.6% of the quadriceps tendon and patellar ligaments, and a hypoechoic defect on the patellar ligament was found in 8.6% of the patients. All these parameters were improved after 12 weeks of MUA. Conclusion: Ultrasound provides morphological changes of the knee joint after MUA, which is helpful to guide the clinical decision of further treatment.