Ten quick tips for computational analysis of medical images

Chicco, Davide; Shiradkar, Rakesh

doi:10.1371/journal.pcbi.1010778

Cited by 13 publications

(7 citation statements)

References 99 publications

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“…The ranking-based method was only applied on the training cohort to reduce set features based on the decrease in Gini impurity. As a rule of thumb, it is necessary to use < 10% of the sample size as the number of features for classification problem [ 30 ]. The final sample size of this study was n = 47; thus, we selected the 4 top ranking features for constructing each ML model.…”

Section: Methodsmentioning

confidence: 99%

Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis

Nakajo,

Hirahara,

Jinguji

et al. 2024

Jpn J Radiol

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Objectives To investigate the usefulness of machine learning (ML) models using pretreatment 18F-FDG-PET-based radiomic features for predicting adverse clinical events (ACEs) in patients with cardiac sarcoidosis (CS). Materials and methods This retrospective study included 47 patients with CS who underwent 18F-FDG-PET/CT scan before treatment. The lesions were assigned to the training (n = 38) and testing (n = 9) cohorts. In total, 49 18F-FDG-PET-based radiomic features and the visibility of right ventricle 18F-FDG uptake were used to predict ACEs using seven different ML algorithms (namely, decision tree, random forest [RF], neural network, k-nearest neighbors, Naïve Bayes, logistic regression, and support vector machine [SVM]) with tenfold cross-validation and the synthetic minority over-sampling technique. The ML models were constructed using the top four features ranked by the decrease in Gini impurity. The AUCs and accuracies were used to compare predictive performances. Results Patients who developed ACEs presented with a significantly higher surface area and gray level run length matrix run length non-uniformity (GLRLM_RLNU), and lower neighborhood gray-tone difference matrix_coarseness and sphericity than those without ACEs (each, p < 0.05). In the training cohort, all seven ML algorithms had a good classification performance with AUC values of > 0.80 (range: 0.841–0.944). In the testing cohort, the RF algorithm had the highest AUC and accuracy (88.9% [8/9]) with a similar classification performance between training and testing cohorts (AUC: 0.945 vs 0.889). GLRLM_RLNU was the most important feature of the modeling process of this RF algorithm. Conclusion ML analyses using 18F-FDG-PET-based radiomic features may be useful for predicting ACEs in patients with CS.

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Section: Methodsmentioning

confidence: 99%

Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis

Nakajo,

Hirahara,

Jinguji

et al. 2024

Jpn J Radiol

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“…It provides very accurate, time & efforts saving, and less expensive diagnosis which can be used in the medical imaging profession [17]. Salivaomics Knowledge Base Saliva (oral fluid) is an emerging biofluid for noninvasive diagnostics used in the detection of human diseases.…”

Section: Applications Of Microarrays In Oral Pathologymentioning

confidence: 99%

Bioinformatics: A New Tool in Dentistry

Saxena,

Srivastava,

Dular

2024

ejmhr

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Bioinformatics is a new speciality that focuses on using information science to solve biological problems. It deals with the collecting, storing, retrieving and analysing data from databases. Bioinformatics has supported and promoted the research in the field of healthcare and has taken it to next level. Bioinformatics can encourage the research in dentistry by understanding the underlying pathways and mechanisms in certain oral diseases. It can also help in early prediction and personalized treatment of cancer that may prove beneficial in early detection and accurate treatment of cancer. Bioinformatics supports in developing patient care databases, image analysis of X- rays, CT and MRI. Diagnostic abilities will also multiple with databases management. Salivanomics is sub-speciality of bioinformatics dealing with saliva knowledge base enabling global exploration of data relevant to saliva. Incorporation of bioinformatics with AI and machine learning can lead to immense positive outcomes in field of research in personalised medicine and gene therapy. This review will help to understand the tools used in bioinformatics and its role in dentistry.

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“…When starting a new computational biology project, one often has the possibility to decide which programming languages, software platforms, and data query languages to use. Similarly to what we recommended for machine learning [ 74 ], pathway enrichment analysis [ 75 ], data cleaning and feature engineering [ 33 ], and medical image analysis [ 76 ], we advocate for using only open-source computer languages and software programs.…”

Section: Introductionmentioning

confidence: 99%

Ten quick tips for avoiding pitfalls in multi-omics data integration analyses

2023

Self Cite

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Data are the most important elements of bioinformatics: Computational analysis of bioinformatics data, in fact, can help researchers infer new knowledge about biology, chemistry, biophysics, and sometimes even medicine, influencing treatments and therapies for patients. Bioinformatics and high-throughput biological data coming from different sources can even be more helpful, because each of these different data chunks can provide alternative, complementary information about a specific biological phenomenon, similar to multiple photos of the same subject taken from different angles. In this context, the integration of bioinformatics and high-throughput biological data gets a pivotal role in running a successful bioinformatics study. In the last decades, data originating from proteomics, metabolomics, metagenomics, phenomics, transcriptomics, and epigenomics have been labelled -omics data, as a unique name to refer to them, and the integration of these omics data has gained importance in all biological areas. Even if this omics data integration is useful and relevant, due to its heterogeneity, it is not uncommon to make mistakes during the integration phases. We therefore decided to present these ten quick tips to perform an omics data integration correctly, avoiding common mistakes we experienced or noticed in published studies in the past. Even if we designed our ten guidelines for beginners, by using a simple language that (we hope) can be understood by anyone, we believe our ten recommendations should be taken into account by all the bioinformaticians performing omics data integration, including experts.

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Ten quick tips for computational analysis of medical images

Cited by 13 publications

References 99 publications

Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis

Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis

Bioinformatics: A New Tool in Dentistry

Ten quick tips for avoiding pitfalls in multi-omics data integration analyses

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