Integrative radiomics expression predicts molecular subtypes of primary clear cell renal cell carcinoma

Yin, Qinye; Hung, Sheng-Che; Rathmell, W. Kimryn; Shen, Liran; Wang, L.; Lin, Weili; Fielding, Julia R.; Khandani, Amir H.; Woods, Michael; Milowsky, Mathew I.; Brooks, Samira A.; Wallen, Eric; Shen, Dinggang

doi:10.1016/j.crad.2018.04.009

Cited by 32 publications

(20 citation statements)

References 32 publications

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“…Yin et al combined positron-emission tomography (PET)/MRI-based radiomics as a surrogate biomarker for intratumoral disease risk of molecular subtype ccA and ccB in patients with primary ccRCC [33]. The sparse partial least squares discriminant analysis method was applied to 168 radiomics features selected from 23 specimens of eight patients.…”

Section: Rcc Subtype Differentiationmentioning

confidence: 99%

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

Suarez-Ibarrola

Basulto-Martínez

Heinze

et al. 2020

Cancers

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Radiomics texture analysis offers objective image information that could otherwise not be obtained by radiologists′ subjective radiological interpretation. We investigated radiomics applications in renal tumor assessment and provide a comprehensive review. A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 March 2020 to identify English literature relevant to radiomics applications in renal tumor assessment. In total, 42 articles were included in the analysis and divided into four main categories: renal mass differentiation, nuclear grade prediction, gene expression-based molecular signatures, and patient outcome prediction. The main area of research involves accurately differentiating benign and malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat and oncocytoma. Nuclear grade prediction may enhance proper patient selection for risk-stratified treatment. Radiomics-predicted gene mutations may serve as surrogate biomarkers for high-risk disease, while predicting patients’ responses to targeted therapies and their outcomes will help develop personalized treatment algorithms. Studies generally reported the superiority of radiomics over expert radiological interpretation. Radiomics provides an alternative to subjective image interpretation for improving renal tumor diagnostic accuracy. Further incorporation of clinical and imaging data into radiomics algorithms will augment tumor prediction accuracy and enhance individualized medicine.

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Section: Rcc Subtype Differentiationmentioning

confidence: 99%

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

Suarez-Ibarrola

Basulto-Martínez

Heinze

et al. 2020

Cancers

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“…In breast cancer and using histopathology images, CNN-based models assigned patients to diagnostic attributes, e.g., tumor stage, and outperformed models based on transcriptomic data only [35]. Examples of other applications of integrative analysis in renal cancer research are provided in [36] and [37].…”

Section: Introductionmentioning

confidence: 99%

Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning

Azuaje

Kim

Hernández

et al. 2019

JCM

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Proteomics data encode molecular features of diagnostic value and accurately reflect key underlying biological mechanisms in cancers. Histopathology imaging is a well-established clinical approach to cancer diagnosis. The predictive relationship between large-scale proteomics and H&E-stained histopathology images remains largely uncharacterized. Here we investigate such associations through the application of machine learning, including deep neural networks, to proteomics and histology imaging datasets generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) from clear cell renal cell carcinoma patients. We report robust correlations between a set of diagnostic proteins and predictions generated by an imaging-based classification model. Proteins significantly correlated with the histology-based predictions are significantly implicated in immune responses, extracellular matrix reorganization, and metabolism. Moreover, we showed that the genes encoding these proteins also reliably recapitulate the biological associations with imaging-derived predictions based on strong gene–protein expression correlations. Our findings offer novel insights into the integrative modeling of histology and omics data through machine learning, as well as the methodological basis for new research opportunities in this and other cancer types.

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“…Recently, Li et al [17] study showed that the PET textures of primary tumor could predict lymphatic metastasis in early-stage cervical carcinoma (AUC = 0.757 in the validation data set; 95% CI, 0.545-0.904; p < 0.05). Other research have also shown that radiomics of primary tumor based on PET images could re ect tumor malignancy and were associated with nodal metastases and molecular subtypes of solid tumor [24,25]. The Rad-score [26], which is calculated by the linear combination of selected features (including histogram and texture parameters) weighted by their respective coe cients selected as informative features, is usually used for radiomics analysis.…”

Section: Discussionmentioning

confidence: 99%

Prediction of lymphovascular space invasion using a combination of tenascin-C, Cox-2, and PET/CT radiomics in patients with early-stage cervical squamous cell carcinoma

Chen

et al. 2020

Preprint

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Background Lymphovascular space invasion is an independent prognostic factor in early-stage cervical cancer. However, there is a lack of non-invasive methods to detect lymphovascular space invasion. Some researchers found that Tenascin-C and Cyclooxygenase-2 was correlated with lymphovascular space invasion. Radiomics has been studied as an emerging tool for distinguishing tumor pathology stage, evaluating treatment response, and predicting prognosis. Materials and methods Eighty-six patients with early-stage cervical squamous cell carcinoma who underwent PET/CT examination were retrospectively analyzed. 401 radiomics features based on PET/CT images were extracted and integrated into radiomics score (Rad-score). Immunohistochemical analysis was performed to evaluate TNC and COX-2 expression. Mann-Whitney U test was used to distinguish differences in the Rad-score, TNC, and COX-2 between LVSI and non-LVSI groups. The correlations of characteristics were tested by Spearman analysis. Machine learning models including radiomics model, protein model and combined model were established by logistic regression algorithm and evaluated by ROC curve. Pairwise comparisons of ROC curves were tested by DeLong test. Results The Rad-score of patients with LVSI was significantly higher than those without. A significant correlation was shown between LVSI and Rad-score (r = 0.631, p < 0.001). TNC was correlated to both the Rad-score (r = 0.244, p = 0.024) and COX-2 (r = 0.227, p = 0.036). The radiomics model had the best predictive performance among all models (AUC = 0.914, p < 0.001) in training dataset. However, in testing dataset, the combined model had better efficiency for predicting LVSI than other models (AUCs: 0.801 vs. 0.756 and 0.801 vs. 0.631, respectively). Conclusion The machine learning model of the combination of PET radiomics with COX-2 and TNC provides a new tool for detecting LVSI in patients with early-stage cervical cancer.

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Integrative radiomics expression predicts molecular subtypes of primary clear cell renal cell carcinoma

Abstract: Featured with high accuracy, an integrated multi-omics model of PET/MRI-based radiomics could be the first non-invasive investigation for disease risk stratification and guidance of treatment in patients with primary ccRCC.

Cited by 32 publications

References 32 publications

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning

Prediction of lymphovascular space invasion using a combination of tenascin-C, Cox-2, and PET/CT radiomics in patients with early-stage cervical squamous cell carcinoma

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