Development and validation of a multi-modal ultrasomics model to predict response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

OBJECTIVES: To assess the performance of multi-modal ultrasomics model to predict efficacy to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) and compare with the clinical model. MATERIALS AND METHODS: This study retrospectively included 106 patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 at our hospital, randomly divided into a training set of 74 and a validation set of 32 in a 7: 3 ratios. Ultrasomics features were extracted from the tumors' region of interest of B-mode ultrasound (BUS) and contrast-enhanced ultrasound (CEUS) images based on PyRadiomics. Mann-Whitney U test, spearman, and least absolute shrinkage and selection operator algorithms were utilized to reduce features dimension. Five models were built with ultrasomics and clinical analysis using multilayer perceptron neural network classifier based on python. Including BUS, CEUS, Combined_1, Combined_2 and Clinical models. The diagnostic performance of models was assessed with the area under the curve (AUC) of the receiver operating characteristic. The DeLong testing algorithm was utilized to compare the models' overall performance. RESULTS: The AUC (95% confidence interval [CI]) of the five models in the validation cohort were as follows: BUS 0.675 (95%CI: 0.481-0.868), CEUS 0.821 (95%CI: 0.660-0.983), Combined_1 0.829 (95%CI: 0.673-0.985), Combined_2 0.893 (95%CI: 0.780-1.000), and Clinical 0.690 (95%CI: 0.509-0.872). The Combined_2 model was the best in the overall prediction performance, showed significantly better compared to the Clinical model after DeLong testing (P < 0.01). Both univariate and multivariate logistic regression analyses showed that age (P < 0.01) and clinical stage (P < 0.01) could be an independent predictor of efficacy after nCRT in patients with LARC. CONCLUSION: The ultrasomics model had better diagnostic performance to predict efficacy to nCRT in patients with LARC than the Clinical model.

A multiparametric clinic-ultrasomics nomogram for predicting extremity soft-tissue tumor malignancy: a combined retrospective and prospective bicentric study.

OBJECTIVE: We aimed at building and testing a multiparametric clinic-ultrasomics nomogram for prediction of malignant extremity soft-tissue tumors (ESTTs). MATERIALS AND METHODS: This combined retrospective and prospective bicentric study assessed the performance of the multiparametric clinic-ultrasomics nomogram to predict the malignancy of ESTTs, when compared with a conventional clinic-radiologic nomogram. A dataset of grayscale ultrasound (US), color Doppler flow imaging (CDFI), and elastography images for 209 ESTTs were retrospectively enrolled from one hospital, and divided into the training and validation cohorts. A multiparametric ultrasomics signature was built based on multimodal ultrasomic features extracted from the grayscale US, CDFI, and elastography images of ESTTs in the training cohort. Another conventional radiologic score was built based on multimodal US features as interpreted by two experienced radiologists. Two nomograms that integrated clinical risk factors and the multiparameter ultrasomics signature or conventional radiologic score were respectively developed. Performance of the two nomograms was validated in the retrospective validation cohort, and tested in a prospective dataset of 51 ESTTs from the second hospital. RESULTS: The multiparametric ultrasomics signature was built based on seven grayscale ultrasomic features, three CDFI ultrasomic features, and one elastography ultrasomic feature. The conventional radiologic score was built based on five multimodal US characteristics. Predictive performance of the multiparametric clinic-ultrasomics nomogram was superior to that of the conventional clinic-radiologic nomogram in the training (area under the receiver operating characteristic curve [AUC] 0.970 vs. 0.890, p = 0.006), validation (AUC: 0.946 vs. 0.828, p = 0.047) and test (AUC: 0.934 vs. 0.842, p = 0.040) cohorts, respectively. Decision curve analysis of combined training, validation and test cohorts revealed that the multiparametric clinic-ultrasomics nomogram had a higher overall net benefit than the conventional clinic-radiologic model. CONCLUSION: The multiparametric clinic-ultrasomics nomogram can accurately predict the malignancy of ESTTs.

Differentiation between combined hepatocellular carcinoma and hepatocellular carcinoma: comparison of diagnostic performance between ultrasomics-based model and CEUS LI-RADS v2017.

BACKGROUND: The imaging findings of combined hepatocellular cholangiocarcinoma (CHC) may be similar to those of hepatocellular carcinoma (HCC). CEUS LI-RADS may not perform well in distinguishing CHC from HCC. Studies have shown that radiomics has an excellent imaging analysis ability. This study aimed to establish and confirm an ultrasomics model for differentiating CHC from HCC. METHODS: Between 2004 and 2016, we retrospectively identified 53 eligible CHC patients and randomly included 106 eligible HCC patients with a ratio of HCC:CHC = 2:1, all of whom were categorized according to Contrast-Enhanced (CE) ultrasonography (US) Liver Imaging Reporting and Data System (LI-RADS) version 2017. The model based on ultrasomics features of CE US was developed in 74 HCC and 37 CHC and confirmed in 32 HCC and 16 CHC. The diagnostic performance of the LI-RADS or ultrasomics model was assessed by the area under the curve (AUC), accuracy, sensitivity and specificity. RESULTS: In the entire and validation cohorts, 67.0% and 81.3% of HCC cases were correctly assigned to LR-5 or LR-TIV contiguous with LR-5, and 73.6% and 87.5% of CHC cases were assigned to LR-M correctly. Up to 33.0% of HCC and 26.4% of CHC were misclassified by CE US LI-RADS. A total of 90.6% of HCC as well as 87.5% of CHC correctly diagnosed by the ultrasomics model in the validation cohort. The AUC, accuracy, sensitivity of the ultrasomics model were higher though without significant difference than those of CE US LI-RADS in the validation cohort. CONCLUSION: The proposed ultrasomics model showed higher ability though the difference was not significantly different for differentiating CHC from HCC, which may be helpful in clinical diagnosis.

Ultrasomics for Early Evaluation of the Tumor Response to MicroRNA-122 in a Nude Mouse Hepatocellular Carcinoma Model.

OBJECTIVES: To explore the value of ultrasomics in temporal monitoring of tumor changes in response to gene therapy in hepatocellular carcinoma compared with methods according to the Response Evaluation Criteria in Solid Tumors (RECIST) and modified RECIST (mRECIST). METHODS: Hepatocellular carcinoma-bearing mice were injected intratumorally with microRNA-122 (miR-122) mimics and an miR-122 negative control in the treatment and control groups, respectively. The injections were performed every 3 days for 5 times (on days 0, 3, 6, 9, and 12). Before each injection and at the experiment ending, 2-dimensional ultrasound imaging was performed for tumor size measurement with RECIST and computing a quantitative imaging analysis with ultrasomics. To analyze the tumor perfusion by mRECIST, perfusion parameters were analyzed offline based on dynamic contrast-enhanced ultrasound image videos using SonoLiver software (TomTec, Unterschleissheim, Germany) on day 13. Tumor miR-122 expression was then analyzed by real-time reverse transcription-polymerase chain reaction experiments. RESULTS: Tumors in mice treated with miR-122 mimics demonstrated a mean ± SD 763- ± 60-fold increase in miR-122 levels compared with tumors in the control group. With RECIST, a significant therapeutic response evaluated by tumor size changes was detected after day 9 (days 9, 12, and 13; P < .001). With mRECIST, no parameters showed significant differences (P > .05). Significant different features of the 2-dimensional ultrasound images between the groups were detected by the ultrasomics analysis, and the model could be successfully built. The ultrasomics score values between the groups were statistically significant after day 6 (days 6, 9, 12, and 13; P < .05). CONCLUSIONS: Ultrasomics revealed significant changes after the second injection of miR-122, showing the potential as an important imaging biomarker for gene therapy.

Construction and validation of an endoscopic ultrasonography-based ultrasomics nomogram for differentiating pancreatic neuroendocrine tumors from pancreatic cancer.

Objectives: To develop and validate various ultrasomics models based on endoscopic ultrasonography (EUS) for retrospective differentiating pancreatic neuroendocrine tumors (PNET) from pancreatic cancer. Methods: A total of 231 patients, comprising 127 with pancreatic cancer and 104 with PNET, were retrospectively enrolled. These patients were randomly divided into either a training or test cohort at a ratio of 7:3. Ultrasomics features were extracted from conventional EUS images, focusing on delineating the region of interest (ROI) for pancreatic lesions. Subsequently, dimensionality reduction of the ultrasomics features was performed by applying the Mann-Whitney test and least absolute shrinkage and selection operator (LASSO) algorithm. Eight machine learning algorithms, namely logistic regression (LR), light gradient boosting machine (LightGBM), multilayer perceptron (MLP), random forest (RF), extra trees, k nearest neighbors (KNN), support vector machine (SVM), and extreme gradient boosting (XGBoost), were employed to train prediction models using nonzero coefficient features. The optimal ultrasomics model was determined using a ROC curve and utilized for subsequent analysis. Clinical-ultrasonic features were assessed using both univariate and multivariate logistic regression. An ultrasomics nomogram model, integrating both ultrasomics and clinical-ultrasonic features, was developed. Results: A total of 107 EUS-based ultrasomics features were extracted, and 6 features with nonzero coefficients were ultimately retained. Among the eight ultrasomics models based on machine learning algorithms, the RF model exhibited superior performance with an AUC= 0.999 (95% CI 0.9977 - 1.0000) in the training cohort and an AUC= 0.649 (95% CI 0.5215 - 0.7760) in the test cohort. A clinical-ultrasonic model was established and evaluated, yielding an AUC of 0.999 (95% CI 0.9961 - 1.0000) in the training cohort and 0.847 (95% CI 0.7543 - 0.9391) in the test cohort. Subsequently, the ultrasomics nomogram demonstrated a significant improvement in prediction accuracy in the test cohort, as evidenced by an AUC of 0.884 (95% CI 0.8047 - 0.9635) and confirmed by the Delong test. The calibration curve and decision curve analysis (DCA) depicted this ultrasomics nomogram demonstrated superior accuracy. They also yielded the highest net benefit for clinical decision-making compared to alternative models. Conclusions: A novel ultrasomics nomogram was proposed and validated, that integrated clinical-ultrasonic and ultrasomics features obtained through EUS, aiming to accurately and efficiently identify pancreatic cancer and PNET.

Implications of a Ultrasomics Signature for Predicting Malignancy in Thyroid Nodules with Hashimoto's Thyroiditis.

RATIONALE AND OBJECTIVES: It remains a challenge to determine the nature of thyroid nodules (TNs) with Hashimoto's thyroiditis (HT). We aim to investigate the multiregional ultrasomics signatures obtained from B-mode ultrasound (B-US) and contrast-enhanced ultrasound (CEUS) images for predicting malignancy in TNs of patients with HT. MATERIALS AND METHODS: B-US and CEUS images of 193 nodules (110 malignant and 83 benign nodules) from 110 patients were retrospectively collected in the single-center study, extracting ultrasomics signatures from the intratumoral (In) and peritumoral (Peri) regions of the thyroid. In-B-US, Peri-B-US, In-CEUS, and Peri-CEUS ultrasomics models and a stacking regression model were constructed, and the diagnostic performance of the models was evaluated by comparing the area under the receiver operating characteristic curve (ROC). RESULTS: The In-B-US, Peri-B-US, In-CEUS, Peri-CEUS, and stacking regression model in the training and testing datasets which attained AUC (95% CI) of 0.872(0.812, 0.932), 0.815(0.747, 0.882), 0.739(0.659, 0.819), 0.890(0.836, 0.943), 0.997(0.992, 1.000) and 0.799(0.650, 0.948), 0.851(0.727, 0.974), 0.622(0.440, 0.805), 0.742(0.573, 0.911), 0.867(0.741, 0.992); sensitivity of 82.8%, 89.7%, 71.3%, 74.7%, 96.6% and 69.6%, 78.3%, 43.5%, 78.3%, 91.3%; specificity of 80.6%, 58.2%, 67.2%, 91.0%, 98.5% and 93.8%, 87.5%, 93.3%, 75.0%, 81.2%, respectively. The stacking regression model based on ultrasomics signatures showed favorable calibration and discriminative capabilities. Compared to the stacking regression model, the difference in AUC between the In-B-US and Peri-B-US models was not statistically significant (P > 0.05). However, the difference in AUC between the In-CEUS and Peri-CEUS models was significant (P < 0.05). CONCLUSION: The application of an ultrasomics approach can effectively predict the benign or malignant nature of TNs accompanied by HT. The diagnostic performance of the ultrasomics model was improved by combining the dual-region and dual-mode of thyroid.

A Preoperative Prediction Model for Lymph Node Metastasis in Patients with Gastric Cancer Using a Machine Learning-based Ultrasomics Approach.

OBJECTIVE: This study aimed to develop an ultrasomics model for predicting lymph node metastasis preoperative in patients with gastric cancer (GC). METHODS: This study enrolled GC patients who underwent preoperative ultrasound examination. Manual segmentation of the region of interest (ROI) was performed by an experienced radiologist to extract radiomics features using the Pyradiomics software. The Z-score algorithm was used for feature normalization, followed by the Wilcoxon test to identify the most informative features. Linear prediction models were constructed using the least absolute shrinkage and selection operator (LASSO). The performance of the ultrasomics model was evaluated using the area under curve (AUC), sensitivity, specificity, and the corresponding 95% confidence intervals (CIs). RESULTS: A total of 464 GC patients (mean age: 60.4 years ±11.3 [SD]; 328 men [70.7%]) were analyzed, of whom 291 had lymph node metastasis. The patients were randomly assigned to either the training (n=324) or test (n=140) sets, using a 7:3 ratio. An ultrasomics model that consisted of 19 radiomics features was developed using Wilcoxon and LASSO algorithms in the training set. Our ultrasomics model showed moderate performance for lymph node metastasis prediction in both the training (AUC: 0.802, 95%CI: 0.752-0.851, P<0.001) and test sets (AUC: 0.802, 95%CI: 0.724-0.879, P<0.001). The calibration curve analysis indicated good agreement between the predicted probabilities of ultrasomics and actual lymph node metastasis status. CONCLUSION: Our study highlights the potential of a machine learning-based ultrasomics model in predicting lymph node metastasis in GC patients, offering implications for personalized therapy approaches.

Ultrasonic Texture Features for Assessing Cardiac Remodeling and Dysfunction.

BACKGROUND: Changes in cardiac size, myocardial mass, cardiomyocyte appearance, and, ultimately, the function of the entire organ are interrelated features of cardiac remodeling that profoundly affect patient outcomes. OBJECTIVES: This study proposes that the application of radiomics for extracting cardiac ultrasonic textural features (ultrasomics) can aid rapid, automated assessment of left ventricular (LV) structure and function without requiring manual measurements. METHODS: This study developed machine-learning models using cardiac ultrasound images from 1,915 subjects in 3 clinical cohorts: 1) an expert-annotated cardiac point-of-care-ultrasound (POCUS) registry (n = 943, 80% training/testing and 20% internal validation); 2) a prospective POCUS cohort for external validation (n = 275); and 3) a prospective external validation on high-end ultrasound systems (n = 484). In a type 2 diabetes murine model, echocardiography of wild-type (n = 10) and Leptr-/- (n = 8) mice were assessed longitudinally at 3 and 25 weeks, and ultrasomics features were correlated with histopathological features of hypertrophy. RESULTS: The ultrasomics model predicted LV remodeling in the POCUS and high-end ultrasound external validation studies (area under the curve: 0.78 [95% CI: 0.68-0.88] and 0.79 [95% CI: 0.73-0.86], respectively). Similarly, the ultrasomics model predicted LV remodeling was significantly associated with major adverse cardiovascular events in both cohorts (P < 0.0001 and P = 0.0008, respectively). Moreover, on multivariate analysis, the ultrasomics probability score was an independent echocardiographic predictor of major adverse cardiovascular events in the high-end ultrasound cohort (HR: 8.53; 95% CI: 4.75-32.1; P = 0.0003). In the murine model, cardiomyocyte hypertrophy positively correlated with 2 ultrasomics biomarkers (R2 = 0.57 and 0.52, Q < 0.05). CONCLUSIONS: Cardiac ultrasomics-based biomarkers may aid development of machine-learning models that provide an expert-level assessment of LV structure and function.

Preoperative prediction of pathological grading of hepatocellular carcinoma using machine learning-based ultrasomics: A multicenter study.

PURPOSE: The present study investigated the value of ultrasomics signatures in the preoperative prediction of the pathological grading of hepatocellular carcinoma (HCC) via machine learning. METHODS: A total of 193 patients were collected from three hospitals. The patients from two hospitals (n = 160) were randomly divided into training set (n = 128) and test set (n = 32) at a 8:2 ratio. The patients from a third hospital were used as an independent validation set (n = 33). The ultrasomics features were extracted from the tumor lesions on the ultrasound images. Support vector machine (SVM) was used to construct three preoperative pathological grading models for HCC on each dataset. The performance of the three models was evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. RESULTS: The ultrasomics signatures extracted from the grayscale ultrasound images could successfully differentiate between high- and low-grade HCC lesions on the training set, test set, and the independent validation set (p < 0.05). On the test set and the validation set, the combined model's performance was the highest, followed by the ultrasomics model and the clinical model successively (p < 0.05). Their AUC (along with 95 %CI) of these models was 0.874(0.709-0.964), 0.789(0.608-0.912), 0.720(0.534-0.863) and 0.849(0.682-0.949), 0.825(0.654-0.935), 0.770(0.591-0.898), respectively. CONCLUSION: Machine learning-based ultrasomics signatures could be used for noninvasive preoperative prediction of pathological grading of HCC. The combined model displayed a better predictive performance for pathological grading of HCC and had a stronger generalization ability.

Preoperative Pathological Grading of Hepatocellular Carcinoma Using Ultrasomics of Contrast-Enhanced Ultrasound.

RATIONALE AND OBJECTIVES: To develop an ultrasomics model for preoperative pathological grading of hepatocellular carcinoma (HCC) using contrast-enhanced ultrasound (CEUS). MATERIAL AND METHODS: A total of 235 HCCs were retrospectively enrolled, including 65 high-grade and 170 low-grade HCCs. Representative images of four-phase CEUS were selected from the baseline sonography, arterial, portal venous, and delayed phase images. Tumor ultrasomics features were automatically extracted using Ultrasomics-Platform software. Models were built via the classifier support vector machine, including an ultrasomics model using the ultrasomics features, a clinical model using the clinical factors, and a combined model using them both. Model performances were tested in the independent validation cohort considering efficiency and clinical usefulness. RESULTS: A total of 1502 features were extracted from each image. After the reproducibility test and dimensionality reduction, 25 ultrasomics features and 3 clinical factors were selected to build the models. In the validation cohort, the combined model showed the best predictive power, with an area under the curve value of 0.785 (95% confidence interval [CI] 0.662-0.909), compared to the ultrasomics model of 0.720 (95% CI 0.576-0.864) and the clinical model of 0.665 (95% CI 0.537-0.793). Decision curve analysis suggested that the combined model was clinically useful, with a corresponding net benefit of 0.760 compared to the other two models. CONCLUSION: We presented an ultrasomics-clinical model based on multiphase CEUS imaging and clinical factors, which showed potential value for the preoperative discrimination of HCC pathological grades.

Machine Learning-Based Ultrasomics Improves the Diagnostic Performance in Differentiating Focal Nodular Hyperplasia and Atypical Hepatocellular Carcinoma.

BACKGROUND: The typical enhancement patterns of hepatocellular carcinoma (HCC) on contrast-enhanced ultrasound (CEUS) are hyper-enhanced in the arterial phase and washed out during the portal venous and late phases. However, atypical variations make a differential diagnosis both challenging and crucial. We aimed to investigate whether machine learning-based ultrasonic signatures derived from CEUS images could improve the diagnostic performance in differentiating focal nodular hyperplasia (FNH) and atypical hepatocellular carcinoma (aHCC). PATIENTS AND METHODS: A total of 226 focal liver lesions, including 107 aHCC and 119 FNH lesions, examined by CEUS were reviewed retrospectively. For machine learning-based ultrasomics, 3,132 features were extracted from the images of the baseline, arterial, and portal phases. An ultrasomics signature was generated by a machine learning model. The predictive model was constructed using the support vector machine method trained with the following groups: ultrasomics features, radiologist's score, and combination of ultrasomics features and radiologist's score. The diagnostic performance was explored using the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 14 ultrasomics features were chosen to build an ultrasomics model, and they presented good performance in differentiating FNH and aHCC with an AUC of 0.86 (95% confidence interval [CI]: 0.80, 0.89), a sensitivity of 76.6% (95% CI: 67.5%, 84.3%), and a specificity of 80.5% (95% CI: 70.6%, 85.9%). The model trained with a combination of ultrasomics features and the radiologist's score achieved a significantly higher AUC (0.93, 95% CI: 0.89, 0.96) than that trained with the radiologist's score (AUC: 0.84, 95% CI: 0.79, 0.89, P < 0.001). For the sub-group of HCC with normal AFP value, the model trained with a combination of ultrasomics features, and the radiologist's score remain achieved the highest AUC of 0.92 (95% CI: 0.87, 0.96) compared to that with the ultrasomics features (AUC: 0.86, 95% CI: 0.74, 0.89, P < 0.001) and radiologist's score (AUC: 0.86, 95% CI: 0.79, 0.91, P < 0.001). CONCLUSIONS: Machine learning-based ultrasomics performs as well as the staff radiologist in predicting the differential diagnosis of FNH and aHCC. Incorporating an ultrasomics signature into the radiologist's score improves the diagnostic performance in differentiating FNH and aHCC.

Study Processes and Applications of Ultrasomics in Precision Medicine.

Ultrasomics is the science of transforming digitally encrypted medical ultrasound images that hold information related to tumor pathophysiology into mineable high-dimensional data. Ultrasomics data have the potential to uncover disease characteristics that are not found with the naked eye. The task of ultrasomics is to quantify the state of diseases using distinctive imaging algorithms and thereby provide valuable information for personalized medicine. Ultrasomics is a powerful tool in oncology but can also be applied to other medical problems for which a disease is imaged. To date there is no comprehensive review focusing on ultrasomics. Here, we describe how ultrasomics works and its capability in diagnosing disease in different organs, including breast, liver, and thyroid. Its pitfalls, challenges and opportunities are also discussed.