To develop and validate a radiomics model for evaluating pathologic complete response (pCR) to neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer (LARC).
We enrolled 222 ...patients (152 in the primary cohort and 70 in the validation cohort) with clinicopathologically confirmed LARC who received chemoradiotherapy before surgery. All patients underwent T2-weighted and diffusion-weighted imaging before and after chemoradiotherapy; 2,252 radiomic features were extracted from each patient before and after treatment imaging. The two-sample
test and the least absolute shrinkage and selection operator regression were used for feature selection, whereupon a radiomics signature was built with support vector machines. Multivariable logistic regression analysis was then used to develop a radiomics model incorporating the radiomics signature and independent clinicopathologic risk factors. The performance of the radiomics model was assessed by its calibration, discrimination, and clinical usefulness with independent validation.
The radiomics signature comprised 30 selected features and showed good discrimination performance in both the primary and validation cohorts. The individualized radiomics model, which incorporated the radiomics signature and tumor length, also showed good discrimination, with an area under the receiver operating characteristic curve of 0.9756 (95% confidence interval, 0.9185-0.9711) in the validation cohort, and good calibration. Decision curve analysis confirmed the clinical utility of the radiomics model.
Using pre- and posttreatment MRI data, we developed a radiomics model with excellent performance for individualized, noninvasive prediction of pCR. This model may be used to identify LARC patients who can omit surgery after chemoradiotherapy.
.
Liver cancer is the second leading cause of cancer‐related deaths, and hepatocellular carcinoma (HCC) is the most common type. Therefore, molecular targets are urgently required for the early ...detection of HCC and the development of novel therapeutic approaches. Glypican‐3 (GPC3), an oncofetal proteoglycan anchored to the cell membrane, is normally detected in the fetal liver but not in the healthy adult liver. However, in HCC patients, GPC3 is overexpressed at both the gene and protein levels, and its expression predicts a poor prognosis. Mechanistic studies have revealed that GPC3 functions in HCC progression by binding to molecules such as Wnt signaling proteins and growth factors. Moreover, GPC3 has been used as a target for molecular imaging and therapeutic intervention in HCC. To date, GPC3‐targeted magnetic resonance imaging, positron emission tomography, and near‐infrared imaging have been investigated for early HCC detection, and various immunotherapeutic protocols targeting GPC3 have been developed, including the use of humanized anti‐GPC3 cytotoxic antibodies, treatment with peptide/DNA vaccines, immunotoxin therapies, and genetic therapies. In this review, we summarize the current knowledge regarding the structure, function, and biology of GPC3 with a focus on its clinical potential as a diagnostic molecule and a therapeutic target in HCC immunotherapy.
This study extends work on independent directors to examine the influence of their human capital and social capital on investor reactions to the board's CEO selection decision. We predict that human ...capital, as represented by the board's CEO experience and industry experience, and social capital, as represented by directors' co-working experience on the board and external directorship ties to other corporate boards, will influence the stock market reactions to new CEO appointments. In a sample of 208 new CEO appointment events in U.S. manufacturing firms between 1999 and 2003, we found that the stock market reacted favorably to the appointments made by boards with higher levels of human and social capital. We also found that the effect of internal social capital was stronger when the new CEO was an insider rather than an outsider. The implications of the results for director selection and CEO succession are discussed.
Display omitted
•Five specific application fields of ML in OSW were identified and analyzed.•Characteristics and suitability of different ML models were summarized.•Most frequently employed ML model ...is ANN, followed by SVM, GA, and DT/RF.•Data scarcity hinders implementation of ML in OSW treatment and recycling.•Low interpretability and unclear selection basis of ML models need to be overcome.
Conventional treatment and recycling methods of organic solid waste contain inherent flaws, such as low efficiency, low accuracy, high cost, and potential environmental risks. In the past decade, machine learning has gradually attracted increasing attention in solving the complex problems of organic solid waste treatment. Although significant research has been carried out, there is a lack of a systematic review of the research findings in this field. This study sorts the research studies published between 2003 and 2020, summarizes the specific application fields, characteristics, and suitability of different machine learning models, and discusses the relevant application limitations and future prospects. It can be concluded that studies mostly focused on municipal solid waste management, followed by anaerobic digestion, thermal treatment, composting, and landfill. The most widely used model is the artificial neural network, which has been successfully applied to various complicated non-linear organic solid waste related problems.
Objectives
To build a dual-energy CT (DECT)–based deep learning radiomics nomogram for lymph node metastasis (LNM) prediction in gastric cancer.
Materials and methods
Preoperative DECT images were ...retrospectively collected from 204 pathologically confirmed cases of gastric adenocarcinoma (mean age, 58 years; range, 28–81 years; 157 men mean age, 60 years; range, 28–81 years and 47 women mean age, 54 years; range, 28–79 years) between November 2011 and October 2018, They were divided into training (
n
= 136) and test (
n
= 68) sets. Radiomics features were extracted from monochromatic images at arterial phase (AP) and venous phase (VP). Clinical information, CT parameters, and follow-up data were collected. A radiomics nomogram for LNM prediction was built using deep learning approach and evaluated in test set using ROC analysis. Its prognostic performance was determined with Harrell’s concordance index (C-index) based on patients’ outcomes.
Results
The dual-energy CT radiomics signature was associated with LNM in two sets (Mann-Whitney
U
test,
p
< 0.001) and an achieved area under the ROC curve (AUC) of 0.71 for AP and 0.76 for VP in test set. The nomogram incorporated the two radiomics signatures and CT-reported lymph node status exhibited AUCs of 0.84 in the training set and 0.82 in the test set. The C-indices of the nomogram for progression-free survival and overall survival prediction were 0.64 (
p
= 0.004) and 0.67 (
p
= 0.002).
Conclusion
The DECT-based deep learning radiomics nomogram showed good performance in predicting LNM in gastric cancer. Furthermore, it was significantly associated with patients’ prognosis.
Key Points
• This study investigated the value of deep learning dual-energy CT–based radiomics in predicting lymph node metastasis in gastric cancer.
• The dual-energy CT–based radiomics nomogram outweighed the single-energy model and the clinical model.
• The nomogram also exhibited a significant prognostic ability for patient survival and enriched radiomics studies.
Objective
To develop and validate a radiomics-based nomogram for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs).
Methods
One hundred ...thirty-eight patients derived from two institutions with pathologically confirmed PNETs (104 in the training cohort and 34 in the validation cohort) were included in this retrospective study. A total of 853 radiomic features were extracted from arterial and portal venous phase CT images respectively. Minimum redundancy maximum relevance and random forest methods were adopted for the significant radiomic feature selection and radiomic signature construction. A fusion radiomic signature was generated by combining both the single-phase signatures. The nomogram based on a comprehensive model incorporating the clinical risk factors and the fusion radiomic signature was established, and decision curve analysis was applied for clinical use.
Results
The fusion radiomic signature has significant association with histologic grade (
p
< 0.001). The nomogram integrating independent clinical risk factor tumor margin and fusion radiomic signature showed strong discrimination with an area under the curve (AUC) of 0.974 (95% CI 0.950–0.998) in the training cohort and 0.902 (95% CI 0.798–1.000) in the validation cohort with good calibration. Decision curve analysis verified the clinical usefulness of the predictive nomogram.
Conclusion
We proposed a comprehensive nomogram consisting of tumor margin and fusion radiomic signature as a powerful tool to predict grade 1 and grade 2/3 PNET preoperatively and assist the clinical decision-making for PNET patients.
Key Points
• Radiomic signature has strong discriminatory ability for the histologic grade of PNETs.
• Arterial and portal venous phase CT imaging are complementary for the prediction of PNET grading.
• The comprehensive nomogram outperformed clinical factors in assisting therapy strategy in PNET patients.
The differential steering can be used not only as the backup system of steer-by-wire, but also as the only steering system. Because the differential steering is realized through the differential ...moment between the coaxial left and right driving wheels, the sharp reduction of the load on the inner driving wheel will directly lead to the failure of the differential steering when the four-wheel independent drive electric vehicle approaches the rollover. Therefore, this paper not only realizes the trajectory tracking of autonomous ground vehicle through the differential steering, but also puts forward the body attitude control to improve the handling stability. Firstly, the dynamic and kinematic models of differential steering autonomous ground vehicle (DSAGV) and its roll model are established, and the linear three-degree of freedom vehicle model is selected as the reference model to generate the ideal body roll angle. Secondly, a model predictive controller (MPC) is designed to control the DSAGV to track the given reference trajectory, and obtain the required differential moment and the resulting front-wheel steering angle. Then, a sliding mode controller (SMC) is adopted to control the DSAGV to track the ideal body roll angle, and obtain the required roll moment. The simulation results show that the proposed MPC and SMC can not only make the DSAGV realize the trajectory tracking, but also achieve the body attitude control.
The wetland classification from remotely sensed data is usually difficult due to the extensive seasonal vegetation dynamics and hydrological fluctuation. This study presents a random forest ...classification approach for the retrieval of the wetland landcover in the arid regions by fusing the Pleiade-1B data with multi-date Landsat-8 data. The segmentation of the Pleiade-1B multispectral image data was performed based on an object-oriented approach, and the geometric and spectral features were extracted for the segmented image objects. The normalized difference vegetation index (NDVI) series data were also calculated from the multi-date Landsat-8 data, reflecting vegetation phenological changes in its growth cycle. The feature set extracted from the two sensors data was optimized and employed to create the random forest model for the classification of the wetland landcovers in the Ertix River in northern Xinjiang, China. Comparison with other classification methods such as support vector machine and artificial neural network classifiers indicates that the random forest classifier can achieve accurate classification with an overall accuracy of 93% and the Kappa coefficient of 0.92. The classification accuracy of the farming lands and water bodies that have distinct boundaries with the surrounding land covers was improved 5%-10% by making use of the property of geometric shapes. To remove the difficulty in the classification that was caused by the similar spectral features of the vegetation covers, the phenological difference and the textural information of co-occurrence gray matrix were incorporated into the classification, and the main wetland vegetation covers in the study area were derived from the two sensors data. The inclusion of phenological information in the classification enables the classification errors being reduced down, and the overall accuracy was improved approximately 10%. The results show that the proposed random forest classification by fusing multi-sensor data can retrieve better wetland landcover information than the other classifiers, which is significant for the monitoring and management of the wetland ecological resources in arid areas.
We designed a borane/gold(I) co‐catalytic system and used it for C−H functionalization reactions and cycloaddition reactions between tertiary amines and α‐alkynylenones. Both reactions effectively ...incorporated a furan ring into the amine.
Gold‐furyl 1,3‐dipoles generated in situ from α‐alkynylenones and gold catalysts were successfully used for borane‐catalyzed amine C−H functionalization reactions, giving amine derivatives bearing a furan ring. The reactions took place either via an α‐furylation pathway or via a 3+2 cycloaddition pathway, depending on the alkyl groups attached to the amine.
Herein, we report a one‐pot method for enantioselective C−H allylation of pyridines at C3 via tandem borane and palladium catalysis. This method involves borane‐catalyzed pyridine hydroboration to ...generate dihydropyridines, then palladium‐catalyzed enantioselective allylation of the dihydropyridines with allylic esters, and finally air oxidation of the allylated dihydropyridines to afford the products. This method enables the introduction of an allylic group at C3 with excellent regio‐ and enantioselectivities.
A one‐pot, three‐step method for highly enantioselective C3‐allylation reactions of pyridines was developed. The method involved borane‐catalyzed dearomative pyridine hydroboration, palladium‐catalyzed enantioselective allylation of the dearomatized intermediate, and finally oxidation by air. The method was applicable to a broad range of pyridines, N‐heteroarenes, and allylic esters.
PDF
