Non-small cell lung carcinoma histopathological subtype phenotyping using high-dimensional multinomial multiclass CT radiomics signature

Khodabakhshi, Zahra; Mostafaei, Shayan; Arabi, Hossein; Oveisi, Mehrdad; Shiri, Isaac; Zaidi, Habib

doi:10.1016/j.compbiomed.2021.104752

Cited by 74 publications

(27 citation statements)

References 56 publications

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“…We included 14,339 patients along with their CT images, segmented the lungs, and extracted distinct radiomics features. As there is no “one fits all” machine learning approaches for radiomic studies, given that their performance is task-dependent and there is large variability across models [ [49] , [50] , [51] , [52] , [53] , [54] , [55] ], we tested the cross-combination of four feature selectors and seven classifiers, which resulted in twenty-eight different combinations of algorithms to find the best performing model. Since the dataset was gathered from different centers, we applied the ComBat Harmonization algorithm that has been successfully applied in radiomics studies over the extracted features [ 25 , 55 , 56 ].…”

Section: Discussionmentioning

confidence: 99%

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Shiri

Salimi

Pakbin

et al. 2022

Computers in Biology and Medicine

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Section: Discussionmentioning

confidence: 99%

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Shiri

Salimi

Pakbin

et al. 2022

Computers in Biology and Medicine

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“…LSTM + Inception attained the highest accuracy and AuC. Another study based on radiomics is the work of Khodbashki et al [17]. A set of 1433 radiomics features were generated from wavelet decomposition and LOG filtered images.…”

Section: Related Workmentioning

confidence: 99%

“…DETECT-LC is contrasted to state of the art studies, which experimented on the TCIA NSCLC datasets. Marentakis et al [16], Chaunzwa et al [15] and Khodbashki et al [17] worked on NSCLC Radiomics dataset, whereas Yang et al [18] studied the performance of their model on a merged dataset of NSCLC Radiomics, NSCLC Radiogenmoics and a private dataset from China Institute. The best results for these related studies are detailed in Table 7.…”

Section: Lung Cancer Pathology Phenotypingmentioning

confidence: 99%

DETECT-LC: A 3D Deep Learning and Textural Radiomics Computational Model for Lung Cancer Staging and Tumor Phenotyping Based on Computed Tomography Volumes

2022

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Lung Cancer is one of the primary causes of cancer-related deaths worldwide. Timely diagnosis and precise staging are pivotal for treatment planning, and thus can lead to increased survival rates. The application of advanced machine learning techniques helps in effective diagnosis and staging. In this study, a multistage neurobased computational model is proposed, DETECT-LC learning. DETECT-LC handles the challenge of choosing discriminative CT slices for constructing 3D volumes, using Haralick, histogram-based radiomics, and unsupervised clustering. ALT-CNN-DENSE Net architecture is introduced as part of DETECT-LC for voxel-based classification. DETECT-LC offers an automatic threshold-based segmentation approach instead of the manual procedure, to help mitigate this burden for radiologists and clinicians. Also, DETECT-LC presents a slice selection approach and a newly proposed relatively light weight 3D CNN architecture to improve existing studies performance. The proposed pipeline is employed for tumor phenotyping and staging. DETECT-LC performance is assessed through a range of experiments, in which DETECT-LC attains outstanding performance surpassing its counterparts in terms of accuracy, sensitivity, F1-score and Area under Curve (AuC). For histopathology classification, DETECT-LC average performance achieved an improvement of 20% in overall accuracy, 0.19 in sensitivity, 0.16 in F1-Score and 0.16 in AuC over the state of the art. A similar enhancement is reached for staging, where higher overall accuracy, sensitivity and F1-score are attained with differences of 8%, 0.08 and 0.14.

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“…In addition, another popular topic is the association of radiomics features often called radiomics signatures with surrogate biomarkers including molecular [83], genomic [66], or path-omics [6] since radiomics are non-distractive and non-invasive; therefore, they can be easily obtained throughout the entire disease continuum. The biggest problems regarding the latter efforts were related to the study design that in the vast majority of the cases was based on a small retrospective cohort [118], coming from a single institution, using hundreds or even thousands [44] of radiomics features extracted from each patient to construct the radiomics signatures possibly using the same dataset after splitting to address multiple target variables, ignoring essential concepts like multiple comparisons corrections and type I errors [26].…”

Section: Usability -For Effective and Beneficial Ai In Medical Imagingmentioning

confidence: 99%

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging

Lekadir,

Osuala,

Gallin

et al. 2021

Preprint

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The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI solutions across the whole value chain of medical imaging, including image reconstruction, medical image segmentation, image-based diagnosis and treatment planning. Notwithstanding the successes and future potential of AI in medical imaging, many stakeholders are concerned of the potential risks and ethical implications of imaging AI solutions, which are perceived as complex, opaque, and difficult to comprehend, utilise, and trust in critical clinical applications. Despite these concerns and risks, there are currently no concrete guidelines and best practices for guiding future AI developments in medical imaging towards increased trust, safety and adoption. To bridge this gap, this paper introduces a careful selection of guiding principles drawn from the accumulated experiences, consensus, and best practices from five large European projects on AI in Health Imaging. These guiding principles are named FUTURE-AI and its building blocks consist of (i) Fairness, (ii) Universality, (iii) Traceability, (iv) Usability, (v) Robustness and (vi) Explainability. In a step-by-step approach, these guidelines are further translated into a framework of concrete recommendations for specifying, developing, evaluating, and deploying technically, clinically and ethically trustworthy AI solutions into clinical practice.

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Non-small cell lung carcinoma histopathological subtype phenotyping using high-dimensional multinomial multiclass CT radiomics signature

Cited by 74 publications

References 56 publications

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

DETECT-LC: A 3D Deep Learning and Textural Radiomics Computational Model for Lung Cancer Staging and Tumor Phenotyping Based on Computed Tomography Volumes

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging

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