Deep learning Radiomics Based on Two-Dimensional Ultrasound for Predicting the Efficacy of Neoadjuvant Chemotherapy in Breast Cancer.

We investigate the predictive value of a comprehensive model based on preoperative ultrasound radiomics, deep learning, and clinical features for pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for the breast cancer. We enrolled 155 patients with pathologically confirmed breast cancer who underwent NAC. The patients were randomly divided into the training set and the validation set in the ratio of 7:3. The deep learning and radiomics features of pre-treatment ultrasound images were extracted, and the random forest recursive elimination algorithm and the least absolute shrinkage and selection operator were used for feature screening and DL-Score and Rad-Score construction. According to multifactorial logistic regression, independent clinical predictors, DL-Score, and Rad-Score were selected to construct the comprehensive prediction model DLRC. The performance of the model was evaluated in terms of its predictive effect, and clinical practicability. Compared to the clinical, radiomics (Rad-Score), and deep learning (DL-Score) models, the DLRC accurately predicted the pCR status, with an area under the curve (AUC) of 0.937 (95%CI: 0.895-0.970) in the training set and 0.914 (95%CI: 0.838-0.973) in the validation set. Moreover, decision curve analysis confirmed that the DLRC had the highest clinical value among all models. The comprehensive model DLRC based on ultrasound radiomics, deep learning, and clinical features can effectively and accurately predict the pCR status of breast cancer after NAC, which is conducive to assisting clinical personalized diagnosis and treatment plan.

Multimodal data integration using machine learning to predict the risk of clear cell renal cancer metastasis: a retrospective multicentre study.

PURPOSE: To develop and validate a predictive combined model for metastasis in patients with clear cell renal cell carcinoma (ccRCC) by integrating multimodal data. MATERIALS AND METHODS: In this retrospective study, the clinical and imaging data (CT and ultrasound) of patients with ccRCC confirmed by pathology from three tertiary hospitals in different regions were collected from January 2013 to January 2023. We developed three models, including a clinical model, a radiomics model, and a combined model. The performance of the model was determined based on its discriminative power and clinical utility. The evaluation indicators included area under the receiver operating characteristic curve (AUC) value, accuracy, sensitivity, specificity, negative predictive value, positive predictive value and decision curve analysis (DCA) curve. RESULTS: A total of 251 patients were evaluated. Patients (n = 166) from Shandong University Qilu Hospital (Jinan) were divided into the training cohort, of which 50 patients developed metastases; patients (n = 37) from Shandong University Qilu Hospital (Qingdao) were used as internal testing, of which 15 patients developed metastases; patients (n = 48) from Changzhou Second People's Hospital were used as external testing, of which 13 patients developed metastases. In the training set, the combined model showed the highest performance (AUC, 0.924) in predicting lymph node metastasis (LNM), while the clinical and radiomics models both had AUCs of 0.845 and 0.870, respectively. In the internal testing, the combined model had the highest performance (AUC, 0.877) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.726 and 0.836, respectively. In the external testing, the combined model had the highest performance (AUC, 0.849) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.708 and 0.804, respectively. The DCA curve showed that the combined model had a significant prediction probability in predicting the risk of LNM in ccRCC patients compared with the clinical model or the radiomics model. CONCLUSION: The combined model was superior to the clinical and radiomics models in predicting LNM in ccRCC patients.

Preparation of a magnetic phosphazene-based hyper crosslinked polymer for the fast and efficient extraction of chlorophenols from water and peach juice.

Two magnetic phosphazene-based hyper crosslinked polymers (M-HCP-OP-TMC and M-HCP-OP-TCL) were newly synthesized by the reaction of Friedel-Crafts acylation, and the M-HCP-OP-TMC showed an excellent extraction capability and rapid adsorption kinetics for chlorophenols as an adsorbent. Then, an efficient analytical method was built for the preconcentration and quantification of chlorophenols from water and peach juice samples by combining M-HCP-OP-TMC based magnetic solid-phase extraction (MSPE) with HPLC-UV detection. The linear response range for the chlorophenols by the method was 0.21-100.0 ng mL-1 for water sample, and 0.36-100.0 ng mL-1 for peach juice sample. The detection limits (S/N = 3) of the proposed method for the analytes were 0.07- 0.25 ng mL-1 and 0.12-0.45 ng mL-1 for water and peach juice samples, respectively. The method recoveries for the spiked samples were in the range of 93.1%-117.1%, and the relative standard deviations were less than 10%. The adsorption of the chlorophenols with the M-HCP-OP-TMC was mainly contributed by π-π stacking and hydrophobic interactions. The results indicate that the method was sensitive and accurate enough for the determination of the chlorophenols from real samples.

A core-shell structured magnetic sulfonated covalent organic framework for the extraction of benzoylureas insecticides from water, pear juice and honey samples.

Three magnetic covalent organic frameworks (named M-TpPa-SO3Na, M-TpPa-SO3H and M-TpPa) were prepared by the solvothermal synthesis method with 1,3,5-trimethylphenol (TP) and either 2-sulfo-1,4-phenylenediamine (Pa-SO3H) or p-phenylenediamine (Pa) as monomers. Among them, the M-TpPa-SO3Na possessed relatively high hydrophilicity, good magnetic responsiveness, and high affinity for the benzoylureas (BUs) insecticides. It was then explored as the magnetic solid-phase extraction adsorbent for the extraction of six BUs (diflubenzuron, triflumuron, hexaflumuron, teflubenzuron, flufenoxuron and chlorfluazuron) from water, pear juice and honey samples prior to high-performance liquid chromatography with ultraviolet detection. Under the optimized experimental conditions, a good linearity was achieved within the concentration range of 0.27-40.0 ng mL-1 for water sample, 0.47-30.0 ng mL-1 for pear juice sample, and 2.70-200.0 ng g-1 for honey sample. The limits of detection for the analytes were 0.08-0.11 ng mL-1 for water sample, 0.14-0.19 ng mL-1 for pear juice sample and 0.80-1.00 ng g-1 for honey sample. The method recoveries for spiked samples were in the range of 85.0%-111.0% with the relative standard deviations less than 8.8%. The developed method was successfully used for the determination of the BUs in water, pear juice and honey samples.