Radiomic features of cervical cancer on T2-and diffusion-weighted MRI: Prognostic value in low-volume tumors suitable for trachelectomy

Wormald, Benjamin W.; Doran, Simon J.; Ind, Thomas; d’Arcy, James A.; Petts, James; deSouza, Nandita M.

doi:10.1016/j.ygyno.2019.10.010

Cited by 33 publications

(23 citation statements)

References 35 publications

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“…Moreover, it was recently reported that the volume of the lesion may impact the value of some T2-w radiomic features, such as dissimilarity and energy, as shown by Wormald et al. ( 35 ). They found that larger cervical cancers had lower dissimilarity and higher energy and thus higher homogeneity and uniformity than smaller ones.…”

Section: Discussionmentioning

confidence: 95%

MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation

et al. 2021

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BackgroundThe differentiation between benign and malignant parotid lesions is crucial to defining the treatment plan, which highly depends on the tumor histology. We aimed to evaluate the role of MRI-based radiomics using both T2-weighted (T2-w) images and Apparent Diffusion Coefficient (ADC) maps in the differentiation of parotid lesions, in order to develop predictive models with an external validation cohort.Materials and MethodsA sample of 69 untreated parotid lesions was evaluated retrospectively, including 37 benign (of which 13 were Warthin’s tumors) and 32 malignant tumors. The patient population was divided into three groups: benign lesions (24 cases), Warthin’s lesions (13 cases), and malignant lesions (32 cases), which were compared in pairs. First- and second-order features were derived for each lesion. Margins and contrast enhancement patterns (CE) were qualitatively assessed. The model with the final feature set was achieved using the support vector machine binary classification algorithm.ResultsModels for discriminating between Warthin’s and malignant tumors, benign and Warthin’s tumors and benign and malignant tumors had an accuracy of 86.7%, 91.9% and 80.4%, respectively. After the feature selection process, four parameters for each model were used, including histogram-based features from ADC and T2-w images, shape-based features and types of margins and/or CE. Comparable accuracies were obtained after validation with the external cohort.ConclusionsRadiomic analysis of ADC, T2-w images, and qualitative scores evaluating margins and CE allowed us to obtain good to excellent diagnostic accuracies in differentiating parotid lesions, which were confirmed with an external validation cohort.

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Section: Discussionmentioning

confidence: 95%

MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation

et al. 2021

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“…Radiomics analyses rely on image acquisition, image analysis and computational statistics [28], so standardisation of these domains is mandatory prior to their validation (Table 1). As radiomics analyses have been applied to CT [29][30][31], MRI [32][33][34][35][36], nuclear medicine using FDG-PET [37][38][39][40][41][42] and other tracers [43,44], and ultrasound [45], image acquisition standardisation needs to consider modality, scanner and scan protocol. Standardisation of image analysis needs to consider software (consistency of technical implementation) and subjectivity (human interaction).…”

Section: Standardising the Radiomics Process For Clinical Trialsmentioning

confidence: 99%

“…Radiomic information on visually imperceptible phenotypic characteristics such as intensity, shape, size and texture distinguish benign and malignant tumours, likely reflecting different cellular morphology [101]. In cervix cancer, radiomic features of low-volume tumours with radiomic profiles similar to high-volume tumours had a worse prognosis implying a more aggressive phenotype at an earlier stage [36]. In a lung cancer study, texture entropy and cluster features, as well as voxel intensity variance features, were associated with the immune system, the p53 pathway, pathways involved in cell cycle regulation [102] and for predicting EGFR mutation status [103].…”

Section: Biological Correlates Of Radiomic Featuresmentioning

confidence: 99%

Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers

et al. 2021

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Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

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“…Advances in imaging analysis have facilitated ways to integrate MRI in treatment prediction [ 20 – 23 ]. A recent multicenter study of 275 patients demonstrated that radiomic features in MRI could potentially identify patients expected to have a favorable response before neoadjuvant chemotherapy [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

Early Metabolic Response Assessed Using 18F-FDG-PET/CT for Image-Guided Intracavitary Brachytherapy Can Better Predict Treatment Outcomes in Patients with Cervical Cancer

et al. 2021

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Purpose This study aimed to identify the prognostic value of early metabolic response assessed using 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) during radiation therapy (RT) for cervical cancer.Materials and Methods We identified 116 patients treated with definitive RT, including FDG-PET/CT–guided intracavitary brachytherapy, between 2009 and 2018. We calculated parameters including maximum (SUVmax) and mean standardized uptake values (SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) for baseline FDG-PET/CT (PETbase) and image-guided brachytherapy planning FDG-PET/CT (PETIGBT). Multivariable analyses of disease-free survival (DFS) and overall survival (OS) were performed.Results We observed a time-dependent decrease in PET parameters between PETbase and PETIGBT; ΔSUVmax, ΔSUVmean, ΔMTV, and ΔTLG were 65%, 61%, 78%, and 93%, respectively. With a median follow-up of 59.5 months, the 5-year DFS and OS rates were 66% and 79%, respectively. Multivariable analysis demonstrated that ΔSUVmax ≥ 50% was associated with favorable DFS (hazard ratio [HR], 2.56; 95% confidence interval [CI], 1.14 to 5.77) and OS (HR, 5.14; 95% CI, 1.55 to 17.01). Patients with ΔSUVmax ≥ 50% (n=87) showed better DFS and OS than those with ΔSUVmax < 50% (n=29) (DFS, 76% vs. 35%, p < 0.001; OS, 90% vs. 41%, p < 0.001, respectively). Adenocarcinoma was frequently observed in ΔSUVmax < 50% compared to ΔSUVmax ≥ 50% (27.6% vs. 10.3%, p=0.003). In addition, models incorporating metabolic parameters showed improved accuracy for predicting DFS (p=0.012) and OS (p=0.004) than models with clinicopathologic factors.Conclusion Changes in metabolic parameters, especially those in SUVmax by > 50%, can help improve survival outcome predictions for patients with cervical cancer treated with definitive RT.

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Radiomic features of cervical cancer on T2-and diffusion-weighted MRI: Prognostic value in low-volume tumors suitable for trachelectomy

Cited by 33 publications

References 35 publications

MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation

MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation

Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers

Early Metabolic Response Assessed Using 18F-FDG-PET/CT for Image-Guided Intracavitary Brachytherapy Can Better Predict Treatment Outcomes in Patients with Cervical Cancer

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