Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months

Rizzo, Stefania; Botta, Francesca; Raimondi, Sara; Origgi, Daniela; Buscarino, Valentina; Colarieti, Anna; Tomao, Federica; Aletti, Giovanni; Zanagnolo, Vanna; Grande, Maria Del; Colombo, Nicoletta; Bellomi, Massimo

doi:10.1007/s00330-018-5389-z

Cited by 104 publications

(65 citation statements)

References 29 publications

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“…Radiomics mainly improves the prediction performance of medical images by improving medical image analysis and using computer algorithms to extract thousands of quantitative features (19). Although these characteristics can reflect tumor biology behavior from various aspects (33), the correlation is also difficult to comprehend between single radiomics features and biological behaviors. Moreover, constructing multi-feature panels is a more common evaluation method (34,35).…”

Section: Discussionmentioning

confidence: 99%

A Novel Multimodal Radiomics Model for Preoperative Prediction of Lymphovascular Invasion in Rectal Cancer

Zhang

Guo

et al. 2020

Front. Oncol.

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Objective: To explore a new predictive model of lymphatic vascular infiltration (LVI) in rectal cancer based on magnetic resonance (MR) and computed tomography (CT). Methods: A retrospective study was conducted on 94 patients with histologically confirmed rectal cancer, they were randomly divided into training cohort (n = 65) and validation cohort (n = 29). All patients underwent MR and CT examination within 2 weeks before treatment. On each slice of the tumor, we delineated the volume of interest on T2-weighted imaging, diffusion weighted imaging, and enhanced CT images, respectively. A total of 1,188 radiological features were extracted from each patient. Then, we used the student t-test or Mann-Whitney U-test, Spearman's rank correlation and least absolute shrinkage and selection operator (LASSO) algorithm to select the strongest features to establish a single and multimodal logic model for predicting LVI. Receiver operating characteristic (ROC) curves and calibration curves were plotted to determine how well they explored LVI prediction performance in the training and validation cohorts. Results: An optimal multi-mode radiology nomogram for LVI estimation was established, which had significant predictive power in training (AUC, 0.884; 95% CI, 0.803-0.964) and validation (AUC, 0.876; 95% CI, 0.721-1.000). Calibration curve and decision curve analysis showed that the multimodal radiomics model provides greater clinical benefits. Conclusion: Multimodal (MR/CT) radiomics models can serve as an effective visual prognostic tool for predicting LVI in rectal cancer. It demonstrated great potential of preoperative prediction to improve treatment decisions.

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Section: Discussionmentioning

confidence: 99%

A Novel Multimodal Radiomics Model for Preoperative Prediction of Lymphovascular Invasion in Rectal Cancer

Zhang

Guo

et al. 2020

Front. Oncol.

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“…A few studies, thus far, have applied radiomics in patients with ovarian cancer, mainly evaluating the ability of texture features to characterize tumor tissue [15,17,39,40] and predict outcome [14,15,17,18,21,22,39]. Best to our knowledge, Table 4 Associations between protein abundance and CT-based imaging traits previously found to be prognostic in patients with HGSOC.…”

Section: Discussionmentioning

confidence: 99%

“…They showed that MRI-derived radiomic features can discriminate between benign and malignant ovarian masses, with a high accuracy of 87% [12]. Furthermore, CT radiomic features of patients with ovarian cancer correlate with response to therapy [13], progression-free survival [14,15], and overall survival [15], and can identify patients at higher risk for recurrence [16]. Recent work by our group focused on evaluating the possible associations between CT imaging traits and texture metrics with genomics data and patient outcome [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis

et al. 2020

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Objectives To investigate the association between CT imaging traits and texture metrics with proteomic data in patients with high-grade serous ovarian cancer (HGSOC). Methods This retrospective, hypothesis-generating study included 20 patients with HGSOC prior to primary cytoreductive surgery. Two readers independently assessed the contrast-enhanced computed tomography (CT) images and extracted 33 imaging traits, with a third reader adjudicating in the event of a disagreement. In addition, all sites of suspected HGSOC were manually segmented texture features which were computed from each tumor site. Three texture features that represented intra-and inter-site tumor heterogeneity were used for analysis. An integrated analysis of transcriptomic and proteomic data identified proteins with conserved expression between primary tumor sites and metastasis. Correlations between protein abundance and various CT imaging traits and texture features were assessed using the Kendall tau rank correlation coefficient and the Mann-Whitney U test, whereas the area under the receiver operating characteristic curve (AUC) was reported as a metric of the strength and the direction of the association. P values < 0.05 were considered significant. Results Four proteins were associated with CT-based imaging traits, with the strongest correlation observed between the CRIP2 protein and disease in the mesentery (p < 0.001, AUC = 0.05). The abundance of three proteins was associated with texture features that represented intra-and inter-site tumor heterogeneity, with the strongest negative correlation between the CKB protein and cluster dissimilarity (p = 0.047, τ = 0.326). Conclusion This study provides the first insights into the potential associations between standard-of-care CT imaging traits and texture measures of intra-and inter-site heterogeneity, and the abundance of several proteins. Key Points • CT-based texture features of intra-and inter-site tumor heterogeneity correlate with the abundance of several proteins in patients with HGSOC. • CT imaging traits correlate with protein abundance in patients with HGSOC.

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“…Studies have shown, for example, dissimilarities in texture metrics between implants to be associated with poorer prognosis. Moving forward, the combination of radiomic features and clinical data may allow the creation of predictive models of resectability or of tumour progression [5]. However, before radiomics is integrated as a clinical adjunct, some hurdles (reproducibility, lack of automation,…) have to be overcome.…”

Section: Current Research and Perspectivesmentioning

confidence: 99%

Early detection of ovarian cancer

Forstner

2020

Eur Radiol

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Early detection is the only way to achieve a high cure rate in women with ovarian cancer. Unfortunately, to date, there is no effective strategy for early detection, despite rapidly emerging biomarkers. The low prevalence of ovarian cancer, low specificity and high rates of false positives have been limitations of screening programs. In the hands of experts, transvaginal sonography and MRI are effective tools to characterise ovarian masses. Currently, ongoing efforts in standardization of technique and analysis are likely to improve diagnostic capabilities in clinical routine, as well as the introduction of predictive risk models of malignancy. Radiomics and radiogenomics potentially offer a broad spectrum of complementary information in ovarian cancer diagnosis and treatment. Key Points • Transvaginal sonography and MRI are effective tools to characterise ovarian masses. • Standardisation of imaging technique and implementation of predictive models of risk of malignancy contribute to early detection of ovarian cancer.

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Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months

Cited by 104 publications

References 29 publications

A Novel Multimodal Radiomics Model for Preoperative Prediction of Lymphovascular Invasion in Rectal Cancer

A Novel Multimodal Radiomics Model for Preoperative Prediction of Lymphovascular Invasion in Rectal Cancer

Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis

Early detection of ovarian cancer

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