A review of radiomics and genomics applications in cancers: the way towards precision medicine

Li, Simin; Zhou, Baosen

doi:10.1186/s13014-022-02192-2

Cited by 26 publications

(21 citation statements)

References 92 publications

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“…Moreover, radiomics was coupled with conventional diagnostic tools in order to augment their sensitivity to detect diseases early in their development [34]. Finally, it was shown to be able to forecast oncological outcomes of patients such as survival, recurrence, response to adjuvant therapy and metastatic progression [35]. The main advantage of radiomics over conventional techniques is that it provides a holistic and noninvasive assessment of the tissues, as opposed to more invasive histopathological tissue analysis methods and RNA sequencing which require biopsies taken from tumor regions.…”

Section: Discussionmentioning

confidence: 99%

Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases

et al. 2023

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Background Finding a noninvasive radiomic surrogate of tumor immune features could help identify patients more likely to respond to novel immune checkpoint inhibitors. Particularly, CD73 is an ectonucleotidase that catalyzes the breakdown of extracellular AMP into immunosuppressive adenosine, which can be blocked by therapeutic antibodies. High CD73 expression in colorectal cancer liver metastasis (CRLM) resected with curative intent is associated with early recurrence and shorter patient survival. The aim of this study was hence to evaluate whether machine learning analysis of preoperative liver CT-scan could estimate high vs low CD73 expression in CRLM and whether such radiomic score would have a prognostic significance. Methods We trained an Attentive Interpretable Tabular Learning (TabNet) model to predict, from preoperative CT images, stratified expression levels of CD73 (CD73High vs. CD73Low) assessed by immunofluorescence (IF) on tissue microarrays. Radiomic features were extracted from 160 segmented CRLM of 122 patients with matched IF data, preprocessed and used to train the predictive model. We applied a five-fold cross-validation and validated the performance on a hold-out test set. Results TabNet provided areas under the receiver operating characteristic curve of 0.95 (95% CI 0.87 to 1.0) and 0.79 (0.65 to 0.92) on the training and hold-out test sets respectively, and outperformed other machine learning models. The TabNet-derived score, termed rad-CD73, was positively correlated with CD73 histological expression in matched CRLM (Spearman’s ρ = 0.6004; P < 0.0001). The median time to recurrence (TTR) and disease-specific survival (DSS) after CRLM resection in rad-CD73High vs rad-CD73Low patients was 13.0 vs 23.6 months (P = 0.0098) and 53.4 vs 126.0 months (P = 0.0222), respectively. The prognostic value of rad-CD73 was independent of the standard clinical risk score, for both TTR (HR = 2.11, 95% CI 1.30 to 3.45, P < 0.005) and DSS (HR = 1.88, 95% CI 1.11 to 3.18, P = 0.020). Conclusions Our findings reveal promising results for non-invasive CT-scan-based prediction of CD73 expression in CRLM and warrant further validation as to whether rad-CD73 could assist oncologists as a biomarker of prognosis and response to immunotherapies targeting the adenosine pathway.

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Section: Discussionmentioning

confidence: 99%

Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases

et al. 2023

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“…Subsequent applications of radiomics have been found in predicting tumor grade and helping radiologists detect challenging precancerous syndromes [ 41 , 42 , 43 , 44 , 45 , 46 ]. The latest fashion in AI application is represented by the role of radiomics in the prediction of response to surgical or medical treatment in cancer patients [ 47 , 48 , 49 , 50 , 51 ]. In this way, radiomics can be used to speculate as to the risk category classification of patients and to predict patient overall survival and risk of complication [ 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ].…”

Section: Introductionmentioning

confidence: 99%

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

et al. 2023

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Background: This paper offers an assessment of radiomics tools in the evaluation of intrahepatic cholangiocarcinoma. Methods: The PubMed database was searched for papers published in the English language no earlier than October 2022. Results: We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, and prediction of staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic tools developed through machine learning, deep learning, and neural network for the recurrence and prediction of biological characteristics. The majority of the studies were retrospective. Conclusions: It is possible to conclude that many performing models have been developed to make differential diagnosis easier for radiologists to predict recurrence and genomic patterns. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.

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“…In 2012, Lambin et al proposed using radiomics, which emphasizes extracting a large amount of information from images (CT, Magnetic Resonance Imaging, and PET-CT) in a high-throughput manner to achieve tumor image segmentation, feature extraction, and model building by virtue of deeper mining, analysis, and prediction of massive image data information to assist physicians in disease diagnosis, prognosis assessment, and treatment response prediction (14). Generally, the radiomics workflow consists of the following main steps: imaging data collection, imaging preprocessing, identification and segmentation of the region or volume of interest, feature extraction, feature selection, model establishment, and model validation (15,16). Some studies have shown that chest CT radiomics can be used to predict benign and malignant lung nodules, aggressiveness, pathological typing, genetic phenotype, and treatment response and to determine prognosis (17)(18)(19)(20)(21).…”

Section: Introductionmentioning

confidence: 99%

A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules

Chen

Geng²,

Song³

et al. 2023

Front. Oncol.

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ObjectiveTo establish a nomogram based on non-enhanced computed tomography(CT) imaging radiomics and clinical features for use in predicting the malignancy of sub-centimeter solid nodules (SCSNs).Materials and methodsRetrospective analysis was performed of records for 198 patients with SCSNs that were surgically resected and examined pathologically at two medical institutions between January 2020 and June 2021. Patients from Center 1 were included in the training cohort (n = 147), and patients from Center 2 were included in the external validation cohort (n = 52). Radiomic features were extracted from chest CT images. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomic feature extraction and computation of radiomic scores. Clinical features, subjective CT findings, and radiomic scores were used to build multiple predictive models. Model performance was examined by evaluating the area under the receiver operating characteristic curve (AUC). The best model was selected for efficacy evaluation in a validation cohort, and column line plots were created.ResultsPulmonary malignant nodules were significantly associated with vascular alterations in both the training (p < 0.001) and external validation (p < 0.001) cohorts. Eleven radiomic features were selected after a dimensionality reduction to calculate the radiomic scores. Based on these findings, three prediction models were constructed: subjective model (Model 1), radiomic score model (Model 2), and comprehensive model (Model 3), with AUCs of 0.672, 0.888, and 0.930, respectively. The optimal model with an AUC of 0.905 was applied to the validation cohort, and decision curve analysis indicated that the comprehensive model column line plot was clinically useful.ConclusionPredictive models constructed based on CT-based radiomics with clinical features can help clinicians diagnose pulmonary nodules and guide clinical decision making.

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A review of radiomics and genomics applications in cancers: the way towards precision medicine

Cited by 26 publications

References 92 publications

Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases

Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules

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