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oncology Oncology

Implementing cone-beam computed tomography-guided online adaptive radiotherapy in cervical cancer.

In Clinical and translational radiation oncology

BACKGROUND AND PURPOSE : Adaptive radiotherapy (ART) in locally advanced cervical cancer (LACC) has shown promising outcomes. This study investigated the feasibility of cone-beam computed tomography (CBCT)-guided online ART (oART) for the treatment of LACC.

MATERIAL AND METHODS : The quality of the automated radiotherapy treatment plans and artificial intelligence (AI)-driven contour delineation for LACC on a novel CBCT-guided oART system were assessed. Dosimetric analysis of 200 simulated oART sessions were compared with standard treatment. Feasibility of oART was assessed from the delivery of 132 oART fractions for the first five clinical LACC patients. The simulated and live oART sessions compared a fixed planning target volume (PTV) margin of 1.5 cm around the uterus-cervix clinical target volume (CTV) with an internal target volume-based approach. Workflow timing measurements were recorded.

RESULTS : The automatically-generated 12-field intensity-modulated radiotherapy plans were comparable to manually generated plans. The AI-driven organ-at-risk (OAR) contouring was acceptable requiring, on average, 12.3 min to edit, with the bowel performing least well and rated as unacceptable in 16 % of cases. The treated patients demonstrated a mean PTV D98% (+/-SD) of 96.7 (+/- 0.2)% for the adapted plans and 94.9 (+/- 3.7)% for the non-adapted scheduled plans (p<10-5). The D2cc (+/-SD) for the bowel, bladder and rectum were reduced by 0.07 (+/- 0.03)Gy, 0.04 (+/-0.05)Gy and 0.04 (+/-0.03)Gy per fraction respectively with the adapted plan (p <10-5). In the live.setting, the mean oART session (+/-SD) from CBCT acquisition to beam-on was 29 +/- 5 (range 21-44) minutes.

CONCLUSION : CBCT-guided oART was shown to be feasible with dosimetric benefits for patients with LACC. Further work to analyse potential reductions in PTV margins is ongoing.

Shelley Charlotte E, Bolt Matthew A, Hollingdale Rachel, Chadwick Susan J, Barnard Andrew P, Rashid Miriam, Reinlo Selina C, Fazel Nawda, Thorpe Charlotte R, Stewart Alexandra J, South Chris P, Adams Elizabeth J

2023-May

Artificial intelligence, Automated treatment planning, Cervical cancer, Cone-beam computed tomography (CBCT), External beam radiotherapy, Image-guided radiotherapy (IGRT), Online adaptive radiotherapy

General General

Multi-Ideology, Multiclass Online Extremism Dataset, and Its Evaluation Using Machine Learning.

In Computational intelligence and neuroscience

Social media platforms play a key role in fostering the outreach of extremism by influencing the views, opinions, and perceptions of people. These platforms are increasingly exploited by extremist elements for spreading propaganda, radicalizing, and recruiting youth. Hence, research on extremism detection on social media platforms is essential to curb its influence and ill effects. A study of existing literature on extremism detection reveals that it is restricted to a specific ideology, binary classification with limited insights on extremism text, and manual data validation methods to check data quality. In existing research studies, researchers have used datasets limited to a single ideology. As a result, they face serious issues such as class imbalance, limited insights with class labels, and a lack of automated data validation methods. A major contribution of this work is a balanced extremism text dataset, versatile with multiple ideologies verified by robust data validation methods for classifying extremism text into popular extremism types such as propaganda, radicalization, and recruitment. The presented extremism text dataset is a generalization of multiple ideologies such as the standard ISIS dataset, GAB White Supremacist dataset, and recent Twitter tweets on ISIS and white supremacist ideology. The dataset is analyzed to extract features for the three focused classes in extremism with TF-IDF unigram, bigrams, and trigrams features. Additionally, pretrained word2vec features are used for semantic analysis. The extracted features in the proposed dataset are evaluated using machine learning classification algorithms such as multinomial Naïve Bayes, support vector machine, random forest, and XGBoost algorithms. The best results were achieved by support vector machine using the TF-IDF unigram model confirming 0.67 F1 score. The proposed multi-ideology and multiclass dataset shows comparable performance to the existing datasets limited to single ideology and binary labels.

Gaikwad Mayur, Ahirrao Swati, Phansalkar Shraddha, Kotecha Ketan, Rani Shalli

2023

General General

Prediction of protein-protein interactions using sequences of intrinsically disordered regions.

In Proteins

Protein-protein interactions (PPIs) play a crucial role in numerous molecular processes. Despite many efforts, mechanisms governing molecular recognition between interacting proteins remain poorly understood and it is particularly challenging to predict from sequence whether two proteins can interact. Here we present a new method to tackle this challenge using intrinsically disordered regions (IDRs). IDRs are protein segments that are functional despite lacking a single invariant three-dimensional structure. The prevalence of IDRs in eukaryotic proteins suggests that IDRs are critical for interactions. To test this hypothesis, we predicted PPIs using IDR sequences in candidate proteins in humans. Moreover, we divide the PPI prediction problem into two specific subproblems and adapt appropriate training and test strategies based on problem type. Our findings underline the importance of defining clearly the problem type and show that sequences encoding IDRs can aid in predicting specific features of the protein interaction network of intrinsically disordered proteins. Our findings further suggest that accounting for IDRs in future analyses should accelerate efforts to elucidate the eukaryotic PPI network.

Kibar Gözde, Vingron Martin

2023-Mar-13

intrinsic disorder, intrinsically disordered proteins, machine learning, prediction, protein-protein interactions

General General

Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions.

In Frontiers in oncology

Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.

Ozdemir Emine Sila, Nussinov Ruth

2023

artificial intelligence, cancer therapeutics, drug discovery, host-pathogen interactions, machine learning, protein-protein interactions

General General

FPUS23: An Ultrasound Fetus Phantom Dataset with Deep Neural Network Evaluations for Fetus Orientations, Fetal Planes, and Anatomical Features

ArXiv Preprint

Ultrasound imaging is one of the most prominent technologies to evaluate the growth, progression, and overall health of a fetus during its gestation. However, the interpretation of the data obtained from such studies is best left to expert physicians and technicians who are trained and well-versed in analyzing such images. To improve the clinical workflow and potentially develop an at-home ultrasound-based fetal monitoring platform, we present a novel fetus phantom ultrasound dataset, FPUS23, which can be used to identify (1) the correct diagnostic planes for estimating fetal biometric values, (2) fetus orientation, (3) their anatomical features, and (4) bounding boxes of the fetus phantom anatomies at 23 weeks gestation. The entire dataset is composed of 15,728 images, which are used to train four different Deep Neural Network models, built upon a ResNet34 backbone, for detecting aforementioned fetus features and use-cases. We have also evaluated the models trained using our FPUS23 dataset, to show that the information learned by these models can be used to substantially increase the accuracy on real-world ultrasound fetus datasets. We make the FPUS23 dataset and the pre-trained models publicly accessible at https://github.com/bharathprabakaran/FPUS23, which will further facilitate future research on fetal ultrasound imaging and analysis.

Bharath Srinivas Prabakaran, Paul Hamelmann, Erik Ostrowski, Muhammad Shafique

2023-03-14

General General

Analyzing the Relationship among Social Capital, Dynamic Capability, and Farmers' Cooperative Performance Using Lightweight Deep Learning Model: A Case Study of Liaoning Province.

In Computational intelligence and neuroscience

The purpose of this study is to understand the relationship between social capital and the performance of Farmers' Cooperatives (Cooperatives) and explore the internal mechanism of social capital affecting the performance of Cooperatives. This work selects two dimensions: cognitive social capital (CSC) and structural social capital (SSC), as indexes to measure the social capital of Cooperatives. An analytical framework is proposed: "Social capital-Dynamic capabilities-Organizational performance." First, according to the characteristics of Cooperatives, it determines the most appropriate index values and preprocesses the original data. Statistical Product and Service Solutions (SPSS) and Analysis of Moment Structure (AMOS) 25.0 software are used for factor analysis. A financial performance evaluation model of Cooperatives based on backpropagation neural network (BPNN) is constructed. Then, based on the survey data of 212 Cooperatives in Liaoning Province, the structural equation model (SEM) is used to test the interaction path between "Social capital-Dynamic capacity-Organizational performance." The results show that SSC's standardized regression coefficients (SRCs) on Cooperatives' economic benefits and member satisfaction are 0.208 and 0.095, respectively, significant at 1%. The actual case analysis concludes that the larger the scale of the structural network embedded in Cooperatives is, the more conducive it is to obtaining extensive resources. As such, Cooperatives can absorb the advanced experience and compensate for the weakness of lack of internal resources and experience. The SRC of CSC on Cooperatives' economic benefits is 0.336, and the P value is 0.204, indicating an insignificant impact of CSC on Cooperatives' economic benefits. This work considers environmental variability, uses dynamic capacity as an independent variable, opens the "black box" between social capital and the performance of Cooperatives, and reveals the intermediate path between the two.

Zhang Simeng, Wu Dongli

2023