Predicting papillary thyroid microcarcinoma in American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) 3 nodules: radiomics analysis based on intratumoral and peritumoral ultrasound images.

Background: A subset of patients undergoing thyroid surgery for presumed benign thyroid disease presented with papillary thyroid microcarcinoma (PTMC). A non-invasive and precise method for early recognition of PTMC are urgently needed. The aim of this study was to construct and validate a nomogram that combines intratumoral and peritumoral radiomics features as well as clinical features for predicting PTMC in the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) 3 nodules using ultrasonography. Methods: A retrospective review was conducted on a cohort of 221 patients who presented with ACR TI-RADS 3 nodules. These patients were subsequently pathologically diagnosed with either PTMC or benign thyroid nodules. These patients were randomly divided into a training and test cohort with an 8:2 ratio for developing the clinical model, intratumor-region model, peritumor-region model and the combined-region model respectively. The radiomics features were extracted from ultrasound (US) images of each patient. We employed K-nearest neighbor (KNN) model as the base model for building the radiomics signature and clinical signature. Finally, a radiomics-clinical nomogram that combined intratumoral and peritumoral radiomics features as well as clinical features was developed. The prediction performance of each model was assessed by the area under the curve (AUC), sensitivity, specificity and calibration curve. Results: A total of 23 radiomics features were selected to develop radiomics models. The combined-region radiomics model showed favorable prediction efficiency in both the training dataset (AUC: 0.955) and the test dataset (AUC: 0.923). A radiomics-clinical nomogram was constructed and achieved excellent calibration and discrimination, which yielded an AUC value of 0.950, a sensitivity of 0.950 and a specificity of 0.920. Conclusions: This study proposed the nomogram that contributes to the accurate and intuitive identification of PTMC in ACR TI-RADS 3 nodules.

The ultrasound-based radiomics-clinical machine learning model to predict papillary thyroid microcarcinoma in TI-RADS 3 nodules.

Background: Conventional ultrasound (CUS) technology has proven to be successful in the identification of thyroid nodules. Moreover, the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) was developed for the purpose of evaluating the risk of thyroid nodules based on ultrasound imaging. Nevertheless, identifying papillary thyroid microcarcinoma (PTMC) from TI-RADS 3 nodules using this system can be difficult due to overlapping morphological features. The main objective of this study was to investigate the efficacy of a machine learning model that utilizes ultrasound-based radiomics features and clinical information in accurately predicting the presence of PTMC in TI-RADS 3 nodules. Methods: A total of 221 patients with TI-RADS 3 nodules were included, consisting of 91 cases of PTMC and 130 benign thyroid nodules. They were randomly divided into training and test cohort in an 8:2 ratio. Radiomics features were extracted from CUS images by manually outlining the targets, while clinical parameters were obtained from electronic medical records. The radiomics model, clinical model, and combined model were constructed and validated to distinguish between PTMC and benign thyroid nodules. Radiomics variables were extracted via the Pyradiomics package (V1.3.0). Moreover, least absolute shrinkage and selection operator (LASSO) regression was used for feature selection. Light Gradient Boosting Machine (LightGBM) was employed to build both radiomics and clinical models. Ultimately, a radiomics-clinical model, which fused radiomics features with clinical information, was developed. Results: Among a total of 1,477 radiomics features, fifteen features that were found to be associated with PTMC through univariate analysis and LASSO regression were selected for the development of the radiomics model. The combined "radiomics-clinical" model demonstrated superior diagnostic accuracy compared to the clinical model for distinguishing PTMC in both the training dataset [area under receiver operating curve (AUC): 0.975 vs. 0.845] and the validation dataset (AUC: 0.898 vs. 0.811). We constructed a radiomics-clinical nomogram, and the clinical applicability was confirmed through decision curve analysis. Conclusions: Utilizing an ultrasound-based radiomics approach has proven to be effective in predicting PTMC in patients with TI-RADS 3 nodules.

Rectal wall dose-volume effect of pre- or post KUSHEN Ningjiaos relationship with 3D brachytherapy in cervical cancer patients.

BACKGROUND: The present prospective study evaluated the safety and efficacy of the rectum following KUSHEN Ningjiaos in cervical cancer. We compared rectal wall changes during brachytherapy with or without KUSHEN Ningjiaos in cervical cancer patients and analyzed the difference in spatial dose distribution, including whole rectum-wall (R-w), anterior rectum-wall (R-a) and posterior rectum-wall (R-p). METHODS AND MATERIALS: One hundred cervical cancer patients with and without KUSHEN Ningjiaos were treated with brachytherapy (600 cGy). The whole R-w was divided into two areas of R-a and R-p, and R-w dose surface map were constructed. The volume of each R-w was compared in patients pre- and post-KUSHEN Ningjiaos. RESULTS: When the pre- vs. post-KUSHEN groups were compared the volume of R-w increased. In the post-KUSHEN group, a significantly higher proportion of the D2cc of VR-w and VR-a compared with the pre-KUSHEN group showed that the D2ccmean increased from 532.45 cGy to 564.7 cGy and 533.51 cGy to 565.26 cGy, respectively; however, results demonstrated a decrease in the D2ccmean of R-p from 260.5 cGy to 240.0868 cGy (P < 0.05). The insertion of KUSHEN Ningjiaos resulted in a reduction of the relative volume of R-p exposed to high doses, and regressive analysis showed that the DR-p-max correlated most strongly with VR-w and D2ccR-p (P < 0.01 and P < 0.05, respectively). CONCLUSION: The insertion of KUSHEN Ningjiaos can protect the rectum. KUSHEN Ningjiaos appears to be safe and well tolerated; therefore, we believe that there will be fewer adverse events after brachytherapy for patients. TRIAL REGISTRATION: A multi-center, prospective clinical trial for KUSHEN Ningjiaos was inserted into rectum to reduce the rate of radiation proctitis in three-dimensional brachytherapy of cervical cancer. ChiCTR1900021631 . 2 Mar 2019-Retrospectively registered.

Interactions between acid and proteins under in vitro gastric condition - a theoretical and experimental quantification.

The gastric digestion of proteins is influenced by the pH and the gastric pH fluctuates after food consumption. However, the dynamics of gastric pH still need to be quantitatively understood. Proteins in food strongly influences the gastric pH. Therefore, we studied the interaction between acid and proteins, including the buffer reaction and the acid diffusion in protein gels. The buffer capacity of proteins stems from its content of ionizable amino acid side groups. Based on this, we set up a model and method to parameterize the buffer capacity of proteins. Moreover, the liberated carboxyl and amino groups during enzymatic hydrolysis of protein can also contribute to the buffer capacity. While we expected protons to diffuse faster than pepsin, we found that the penetration distance of acid is comparable to that of pepsin. The buffer reaction caused the acid to concentrate tenfold in the gel compared to the bulk acid concentration. Therefore, we postulated that the buffer reaction reduces acid diffusivity in gels.