Reliability as a Precondition for Trust—Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction

Müller‐Franzes, Gustav; Nebelung, Sven; Schock, Justus; Haarburger, Christoph; Khader, Firas; Pedersoli, Federico; Schulze-Hagen, Maximilian; Kühl, Christiane; Truhn, Daniel

doi:10.3390/diagnostics12020247

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…For CT radiomics, radiologists must observe each layer of images before segmenting the lesions, but some large lesions have dozens of tomographic images. Variability of features among operators performing segmentation makes image analysis-based machine learning (ML) nontransferable [ 19 ]. Automatic and semi-automatic segmentation methods are functions carried by some open-source software that can automatically identify and segment ROIs or partial regions.…”

Section: Workflow Of Ultrasound Radiomicsmentioning

confidence: 99%

A Review of the Role of Ultrasound Radiomics and Its Application and Limitations in the Investigation of Thyroid Disease

Zhang

2022

Med Sci Monit

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The incidence of thyroid disease has gradually increased in recent years. Conventional ultrasound is one of the most critical thyroid imaging methods, but it still has certain limitations. The use of B-model ultrasound (BMUS) diagnosis of thyroid disease will be affected by a doctors’ clinical experience. The ultrasound radiomics is based on ultrasound images to delineate the region of interest (ROI), and then extract features to quantify the disease information contained in the image, which helps to analyze the correlation between the image and the clinical pathology of the disease. By building a powerful model, it can be used to diagnose benign and malignant thyroid nodules, predict lymph node status in thyroid cancer, analyze molecular biological characteristics, and predict the survival of thyroid cancer patients. At present, the application of ultrasound radiomics in the thyroid is pervasive. These ultrasound radiomics studies have further promoted the progress of ultrasonic technology in the field of thyroid disease. Clinicians should be familiar with the workflow of ultrasound radiomics and understand the application of this technology to the thyroid. In this article, we first describe the workflow of ultrasound radiomics, followed by an overview of the application of ultrasound radiomics to the thyroid. Finally, some current limitations of the technology and areas for future improvement are discussed. This article aims to review the role of ultrasound radiomics and its application and limitations in the investigation of thyroid disease.

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Section: Workflow Of Ultrasound Radiomicsmentioning

confidence: 99%

A Review of the Role of Ultrasound Radiomics and Its Application and Limitations in the Investigation of Thyroid Disease

Zhang

2022

Med Sci Monit

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Attention-Based Multimodal Bilinear Feature Fusion for Lung Cancer Survival Analysis

Na,

Wang,

Zhuang

et al. 2023

2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE)

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Are deep models in radiomics performing better than generic models? A systematic review

Demircioğlu

2023

Eur Radiol Exp

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Background Application of radiomics proceeds by extracting and analysing imaging features based on generic morphological, textural, and statistical features defined by formulas. Recently, deep learning methods were applied. It is unclear whether deep models (DMs) can outperform generic models (GMs). Methods We identified publications on PubMed and Embase to determine differences between DMs and GMs in terms of receiver operating area under the curve (AUC). Results Of 1,229 records (between 2017 and 2021), 69 studies were included, 61 (88%) on tumours, 68 (99%) retrospective, and 39 (56%) single centre; 30 (43%) used an internal validation cohort; and 18 (26%) applied cross-validation. Studies with independent internal cohort had a median training sample of 196 (range 41–1,455); those with cross-validation had only 133 (43–1,426). Median size of validation cohorts was 73 (18–535) for internal and 94 (18–388) for external. Considering the internal validation, in 74% (49/66), the DMs performed better than the GMs, vice versa in 20% (13/66); no difference in 6% (4/66); and median difference in AUC 0.045. On the external validation, DMs were better in 65% (13/20), GMs in 20% (4/20) cases; no difference in 3 (15%); and median difference in AUC 0.025. On internal validation, fused models outperformed GMs and DMs in 72% (20/28), while they were worse in 14% (4/28) and equal in 14% (4/28); median gain in AUC was + 0.02. On external validation, fused model performed better in 63% (5/8), worse in 25% (2/8), and equal in 13% (1/8); median gain in AUC was + 0.025. Conclusions Overall, DMs outperformed GMs but in 26% of the studies, DMs did not outperform GMs.

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Reliability as a Precondition for Trust—Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction

Cited by 4 publications

References 32 publications

A Review of the Role of Ultrasound Radiomics and Its Application and Limitations in the Investigation of Thyroid Disease

A Review of the Role of Ultrasound Radiomics and Its Application and Limitations in the Investigation of Thyroid Disease

Attention-Based Multimodal Bilinear Feature Fusion for Lung Cancer Survival Analysis

Are deep models in radiomics performing better than generic models? A systematic review

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