Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

arXiv (Cornell University), Journal Year: 2018, Volume and Issue: unknown

Published: Jan. 1, 2018

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as sub-regions depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting biological properties. Their shape, extent, location some factors that make these tumors difficult to resect, cases inoperable. The amount resected tumor a factor considered longitudinal when evaluating apparent for potential diagnosis progression. Furthermore, there mounting evidence accurate segmentation can offer basis quantitative image analysis towards prediction patient overall survival. study assesses state-of-the-art machine learning (ML) methods used mpMRI during last seven instances International Brain Tumor Segmentation (BraTS) challenge, 2012-2018. Specifically, we focus on i) segmentations glioma pre-operative ii) assessing progression virtue growth beyond use RECIST/RANO criteria, iii) predicting survival from scans patients underwent gross total resection. Finally, investigate challenge identifying best ML algorithms each tasks, considering apart being diverse instance multi-institutional BraTS dataset has been continuously evolving/growing dataset.

Language: Английский

---

Radiomics in medical imaging—“how-to” guide and critical reflection

Insights into Imaging, Journal Year: 2020, Volume and Issue: 11(1)

Published: Aug. 12, 2020

Abstract Radiomics is a quantitative approach to medical imaging, which aims at enhancing the existing data available clinicians by means of advanced mathematical analysis. Through extraction spatial distribution signal intensities and pixel interrelationships, radiomics quantifies textural information using analysis methods from field artificial intelligence. Various studies different fields in imaging have been published so far, highlighting potential enhance clinical decision-making. However, faces several important challenges, are mainly caused various technical factors influencing extracted radiomic features. The aim present review twofold: first, we typical workflow deliver practical “how-to” guide for Second, discuss current limitations radiomics, suggest improvements, summarize relevant literature on subject.

Language: Английский

---

Deep learning in histopathology: the path to the clinic

Nature Medicine, Journal Year: 2021, Volume and Issue: 27(5), P. 775 - 784

Published: May 1, 2021

Language: Английский

---

AI applications to medical images: From machine learning to deep learning

Physica Medica, Journal Year: 2021, Volume and Issue: 83, P. 9 - 24

Published: March 1, 2021

Language: Английский

---

Predicting cancer outcomes with radiomics and artificial intelligence in radiology

Nature Reviews Clinical Oncology, Journal Year: 2021, Volume and Issue: 19(2), P. 132 - 146

Published: Oct. 18, 2021

Language: Английский

---

AI in Medical Imaging Informatics: Current Challenges and Future Directions

IEEE Journal of Biomedical and Health Informatics, Journal Year: 2020, Volume and Issue: 24(7), P. 1837 - 1857

Published: May 29, 2020

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing practice. More specifically, it summarizes advances in acquisition technologies different modalities, highlighting necessity efficient data management strategies context AI big healthcare analytics. It then a synopsis contemporary emerging algorithmic methods disease classification organ/ tissue segmentation, focusing on deep learning architectures that have already become de facto approach. The benefits in-silico modelling linked with evolving 3D reconstruction visualization applications are further documented. Concluding, integrative analytics approaches driven by associate branches highlighted this study promise to revolutionize informatics as known today continuum both radiology digital pathology applications. latter, is projected enable informed, more accurate diagnosis, timely prognosis, effective treatment planning, underpinning precision medicine.

Language: Английский

---

The Biological Meaning of Radiomic Features

Radiology, Journal Year: 2021, Volume and Issue: 298(3), P. 505 - 516

Published: Jan. 5, 2021

Radiomic analysis offers a powerful tool for the extraction of clinically relevant information from radiologic imaging. Radiomics can be used to predict patient outcome through automated high-throughput feature extraction, using large training cohorts elucidate subtle relationships between image characteristics and disease status. However powerful, data-driven nature radiomics inherently no insight into biological underpinnings observed relationships. Early work was dominated by semantic, radiologist-defined features carried qualitative real-world meaning. Following rapid developments popularity machine learning approaches, field moved quickly toward agnostic analyses, resulting in increasingly sets. This trend took focus an increase predictive power further away understanding findings. Such disconnect predictor model meaning will limit broad clinical translation. Efforts reintroduce are gaining traction with distinct emerging approaches available, including genomic correlates, local microscopic pathologic textures, macroscopic histopathologic marker expression. These methods presented this review, their significance is discussed. The authors that following increasing pressure robust radiomics, validation become standard practice field, thus cementing role method decision making. © RSNA, 2021 An earlier incorrect version appeared online. article corrected on February 10, 2021.

Language: Английский

---

Harnessing multimodal data integration to advance precision oncology

Nature reviews. Cancer, Journal Year: 2021, Volume and Issue: 22(2), P. 114 - 126

Published: Oct. 18, 2021

Language: Английский

---

From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and Opportunities

IEEE Signal Processing Magazine, Journal Year: 2019, Volume and Issue: 36(4), P. 132 - 160

Published: June 26, 2019

Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping hospitals the availability extensive set images through internal/external communication systems, have resulted a recent surge significant interest "Radiomics". Radiomics is an emerging relatively new research field, which refers to extracting semi-quantitative and/or quantitative features from goal developing predictive prognostic models, expected become critical component for integration image-derived information personalized treatment near future. The conventional workflow typically based on pre-designed (also referred as hand-crafted or engineered features) segmented region interest. Nevertheless, deep caused trends towards learning-based discovery Radiomics). Considering advantages these two approaches, there are also hybrid solutions developed exploit potentials multiple data sources. variety approaches Radiomics, further improvements require comprehensive integrated sketch, this article. This manuscript provides unique interdisciplinary perspective by discussing state-of-the-art context Radiomics.

Language: Английский

---

A deep look into radiomics

La radiologia medica, Journal Year: 2021, Volume and Issue: 126(10), P. 1296 - 1311

Published: July 2, 2021

Abstract Radiomics is a process that allows the extraction and analysis of quantitative data from medical images. It an evolving field research with many potential applications in imaging. The purpose this review to offer deep look into radiomics, basis, deeply discussed technical point view, through main applications, challenges have be addressed translate clinical practice. A detailed description techniques used various steps radiomics workflow, which includes image acquisition, reconstruction, pre-processing, segmentation, features analysis, here proposed, as well overview promising results achieved focusing on limitations possible solutions for implementation. Only in-depth comprehensive current methods can suggest power fostering precision medicine thus care patients, especially cancer detection, diagnosis, prognosis treatment evaluation.

Language: Английский

---