Deep learning takes on tumours

Landhuis, Esther

doi:10.1038/d41586-020-01128-8

Cited by 31 publications

(9 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SA growth rate increased mainly in the left and right middle occipital gyri, right lingual gyrus area, and right cuneus ( Hazlett et al, 2017 ). Differences in the right occipital lobe are consistent with other studies ( Irimia et al, 2018 ; Landhuis, 2020 ) that explain visual perception differences between ASD patients and HCs. This appears to correlate with a report by Irimia et al (2018) that discovered the ASD group had higher areas and connectivity densities in the cuneus, occipital lobes, and the superior and transverse occipital sulci than the HC group.…”

Section: Highlighted Researchsupporting

confidence: 92%

Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

Bahathiq

Banjar

Bamaga

et al. 2022

Front. Neuroinform.

View full text Add to dashboard Cite

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects approximately 1% of the population and causes significant burdens. ASD’s pathogenesis remains elusive; hence, diagnosis is based on a constellation of behaviors. Structural magnetic resonance imaging (sMRI) studies have shown several abnormalities in volumetric and geometric features of the autistic brain. However, inconsistent findings prevented most contributions from being translated into clinical practice. Establishing reliable biomarkers for ASD using sMRI is crucial for the correct diagnosis and treatment. In recent years, machine learning (ML) and specifically deep learning (DL) have quickly extended to almost every sector, notably in disease diagnosis. Thus, this has led to a shift and improvement in ASD diagnostic methods, fulfilling most clinical diagnostic requirements. However, ASD discovery remains difficult. This review examines the ML-based ASD diagnosis literature over the past 5 years. A literature-based taxonomy of the research landscape has been mapped, and the major aspects of this topic have been covered. First, we provide an overview of ML’s general classification pipeline and the features of sMRI. Next, representative studies are highlighted and discussed in detail with respect to methods, and biomarkers. Finally, we highlight many common challenges and make recommendations for future directions. In short, the limited sample size was the main obstacle; Thus, comprehensive data sets and rigorous methods are necessary to check the generalizability of the results. ML technologies are expected to advance significantly in the coming years, contributing to the diagnosis of ASD and helping clinicians soon.

show abstract

Section: Highlighted Researchsupporting

confidence: 92%

Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

Bahathiq

Banjar

Bamaga

et al. 2022

Front. Neuroinform.

View full text Add to dashboard Cite

show abstract

“…The Chinese Cervical Cancer Clinical (Four-C) Study was created in 2014 with the aim of collecting clinical and prognostic information on patients diagnosed with cervical cancer in mainland China since 2004. Its research objectives currently focus on four main themes: (i) to explore the associations of therapeutic strategies with complications as well as mid-and long-term clinical outcomes, including comparative effectiveness research based on marginal structural models or propensity scores (25-27); (ii) to widely evaluate the prognostic factors of cervical cancer (such as late access to care and the influence of nutritional status) and then guide treatment as well as care options, and to precisely predict the prognosis of patients so as to develop much more effective program of personalized followup and intervention (21,22); (iii) to utilize artificial intelligence (AI) and machine learning (ML) approaches for multimodal data aggregation and multifactorial examination in order to develop a knowledge base of cervical clinical auxiliary diagnosis and prognostic prediction (28)(29)(30). What' more, as the Four-C Study relatively represents the occurrence of cervical cancer across mainland China in terms of age, geographical origin, year of diagnosis, clinical stage, gross type, and histological type, it can also serve to map the burden of cervical cancer in different districts and monitor trends in incidence of cervical cancer, which could potentially inform prevention and control strategies (31).…”

Section: Why Was the Cohort Set Up?mentioning

confidence: 99%

Cohort Profile: Chinese Cervical Cancer Clinical Study

Zhang

Sun

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

Cervical cancer is the fourth most common cancer worldwide, but its incidence varies greatly in different countries. Regardless of incidence or mortality, the burden of cervical cancer in China accounts for approximately 18% of the global burden. The Chinese Cervical Cancer Clinical Study is a hospital-based multicenter open cohort. The major aims of this study include (i) to explore the associations of therapeutic strategies with complications as well as mid- and long-term clinical outcomes; (ii) to widely assess the factors which may have an influence on the prognosis of cervical cancer and then guide the treatment options, and to estimate prognosis using a prediction model for precise post-treatment care and follow-up; (iii) to develop a knowledge base of cervical clinical auxiliary diagnosis and prognosis prediction using artificial intelligence and machine learning approaches; and (iv) to roughly map the burden of cervical cancer in different districts and monitoring the trend in incidence of cervical cancer to potentially inform prevention and control strategies. Patients eligible for inclusion were those diagnosed with cervical cancer, whether during an outpatient visit or hospital admission, at 47 different types of medical institutions in 19 cities of 11 provinces across mainland China between 2004 and 2018. In a total, 63 926 patients with cervical cancer were enrolled in the cohort. Since the project inception, a large number of standardized variables have been collected, including epidemiological characteristics, cervical cancer-related symptoms, physical examination results, laboratory testing results, imaging reports, tumor biomarkers, tumor staging, tumor characteristics, comorbidities, co-infections, treatment and short-term complications. Follow-up was performed at least once every 6 months within the first 5 years after receiving treatment and then annually thereafter. At present, we are developing a cervical cancer imaging database containing Dicom files with data of computed tomography/magnetic resonance imaging examination. Additionally, we are also collecting original pathological specimens of patients with cervical cancer. Potential collaborators are welcomed to contact the corresponding authors, and anyone can submit at least one specific study proposal describing the background, objectives and methods of the study.

show abstract

“…Machine learning classification techniques have been applied in cancer research to identify and classify the types of cancer cells with relatively high accuracy, sensitivity and specificity. Some popular applications involved Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Artificial Neural Networks (ANNs), Decision Tree (DT), Random Forest (RF) and Bayesian Networks (BNs) [121,122,[127][128][129][130][131]. In addition to cancer, the classification of microscopic red blood cells images from hematological disorder, such as sickle cell disease using deep-CNNs were able to reveal a diverse and any alteration in the cell shapes related to their biomechanical and bio-rheological characteristics.…”

Section: Technical Recommendation Of Machine Learning In Stem Cells Researchmentioning

confidence: 99%

Machine Learning in Stem Cells Research: Application for Biosafety and Bioefficacy Assessment

et al. 2021

View full text Add to dashboard Cite

The applicability of machine learning-based analysis in the field of biomedical field has been very beneficial in determining the biological mechanism and validation for a wide range of biological scenarios. This approach is also gaining momentum in variou s stem cells research activities, specifically for stem cells characterization and differentiation pattern. The adoption of similar computational approaches to study and assess biosafety and bioefficacy risks of stem cells for clinical application is the n ext progression. In particular where tumorigenicity has been one of the major concerns in stem cells therapy. There are many factors influencing tumorigenicity in stem cells which may be difficult to capture under conventional laboratory settings. In addition, given the possible multifactorial etiology of tumorigenicity, defining a onesize-fits-all strategy to test such risk in stem cells might not be feasible and may compromise stem cells safety and effectiveness in therapy. Given the increase in biological datasets (which is no longer limited to genomic data) and the advancement of health informatics powered by state-of-the-art machine learning algorithms, there exists a potential for practical application in biosafety and bioefficacy of stem cells therap y. Here, we identified relevant machine learning approaches and suggested protocols intended for stem cells research focusing on the possibility of its usage for stem cells biosafety and bioefficacy assessment. Ultimately, generating models that may assist healthcare professionals to make a better-informed decision in stem cell therapy.

show abstract

Deep learning takes on tumours

Cited by 31 publications

References 10 publications

Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

Cohort Profile: Chinese Cervical Cancer Clinical Study

Machine Learning in Stem Cells Research: Application for Biosafety and Bioefficacy Assessment

Contact Info

Product

Resources

About