Development and validation of MRI-based radiomics model to predict recurrence risk in patients with endometrial cancer: a multicenter study

Lin, Zijing; Wang, Ting; Li, Qiong; Bi, Qiu; Wang, Yaoxin; Luo, Yingwei; Feng, Feng; Xiao, Meiling; Gu, Yajia; Qiang, Jin Wei; Li, Haiming

doi:10.1007/s00330-023-09685-y

Cited by 7 publications

(4 citation statements)

References 46 publications

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“…However, in this study, the performance of the mediastinum-based model was lower compared to the performance of the intratumor-based model. This is similar to past studies on other tumors [12,13]. The following reasons may explain these results.…”

Section: Relationship Of Rs and Combined Radiomics Model To Pfs After...supporting

confidence: 92%

Tumor Radiomics Features on Pretreatment CT to Predict Response to First-Line Chemotherapy in Thymic Carcinoma: A Multicenter Study

Wu,

Wang,

Fan

et al. 2024

Preprint

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Objectives Platinum-based chemotherapy as first-line treatment have shown promising results against tumors in thymic carcinoma (TC). However, clinical benefit has been limited to a small proportion of patients. We developed and validated a combined radiomics model to predict progressive disease (PD) in patients suffering from TC following first-line chemotherapy.Methods Patients receiving platinum-based chemotherapy as first-line treatment from four centers in Shandong, China, were retrospectively included (n = 134); 93 and 41 were entered into the training and validation sets. Radiomics features were extracted from pretreatment enhanced CT. After feature selection, radiomics score (RS) was developed by Linear Discriminant Analysis (LDA) and TNM, clinicopathological and clinicopathological-radiomics models (combined radiomics model) were developed by using logistic regression algorithm. Models were assessed for performance, incremental predictive value of radiomics features versus clinicopathological features, and the relationship of RS and clinical factors to survival.Results The clinicopathological model was a modest predictor of PD, with area under curve (AUC) of 0.879 (95% CI: 0.709–0.890) and 0.799 (95% CI: 0.778–0.980) in the training and validation sets. The AUC of the combined radiomics model was 0.937 (95% CI: 0.891–0.984) and 0.899 (95% CI: 0.800-0.997), which is of good calibration and clinical application. The incremental predictive value of radiomics features for clinicopathological features was 27% (p < 0.001) and 0.4% (p = 0.108) in the training and validation sets. In addition, RS, CD5, and distant metastasis were associated with progression-free survival in both the training and validation sets.Conclusion Radiomics features extracted from pretreatment enhanced CT allow prediction of individualized objective responses to platinum-based chemotherapy as first-line treatment in TC, providing incremental predictive value for clinicopathological features, and are associated with progression-free survival after initiation of this combination regimen.

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Section: Relationship Of Rs and Combined Radiomics Model To Pfs After...supporting

confidence: 92%

Tumor Radiomics Features on Pretreatment CT to Predict Response to First-Line Chemotherapy in Thymic Carcinoma: A Multicenter Study

Wu,

Wang,

Fan

et al. 2024

Preprint

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“…The texture features can serve as a biomarker for predicting the presence of clinically remarkable PCa [ 23 ]. The wavelet filter disassembles the original images in various directions and reveals multidimensional spatial heterogeneity, which can assist in revealing tumor heterogeneity that may not be detectable in the original images [ 24 , 25 ]. Therefore, we concluded that the texture and wavelet features might be the most helpful in predicting GS and positive needles of systematic biopsy to estimate PCa aggressiveness.…”

Section: Discussionmentioning

confidence: 99%

Prediction of prostate cancer aggressiveness using magnetic resonance imaging radiomics: a dual-center study

Pan,

Shi,

et al. 2024

Discov Onc

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Purpose The Gleason score (GS) and positive needles are crucial aggressive indicators of prostate cancer (PCa). This study aimed to investigate the usefulness of magnetic resonance imaging (MRI) radiomics models in predicting GS and positive needles of systematic biopsy in PCa. Material and Methods A total of 218 patients with pathologically proven PCa were retrospectively recruited from 2 centers. Small-field-of-view high-resolution T2-weighted imaging and post-contrast delayed sequences were selected to extract radiomics features. Then, analysis of variance and recursive feature elimination were applied to remove redundant features. Radiomics models for predicting GS and positive needles were constructed based on MRI and various classifiers, including support vector machine, linear discriminant analysis, logistic regression (LR), and LR using the least absolute shrinkage and selection operator. The models were evaluated with the area under the curve (AUC) of the receiver-operating characteristic. Results The 11 features were chosen as the primary feature subset for the GS prediction, whereas the 5 features were chosen for positive needle prediction. LR was chosen as classifier to construct the radiomics models. For GS prediction, the AUC of the radiomics models was 0.811, 0.814, and 0.717 in the training, internal validation, and external validation sets, respectively. For positive needle prediction, the AUC was 0.806, 0.811, and 0.791 in the training, internal validation, and external validation sets, respectively. Conclusions MRI radiomics models are suitable for predicting GS and positive needles of systematic biopsy in PCa. The models can be used to identify aggressive PCa using a noninvasive, repeatable, and accurate diagnostic method.

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“…Concerning EC recurrence risk assessment, Lin and co-workers in their retrospective multicentric research built a model based on clinicopathological and radiomics features extracted from the intra-tumoral area of 421 MRIs. This mode showed optimal performance in predicting the recurrence in terms of AUCs (0.87 and 0.85 in the internal and external validation cohorts, respectively), calibration curve, and decision curve analysis [58].…”

Section: Prognosismentioning

confidence: 98%

Radiomics in Gynaecological Imaging: A State-of-the-Art Review

Franco,

Vernuccio,

Maino

et al. 2023

Applied Sciences

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Radiomics is an emerging field of research based on extracting mathematical descriptive features from medical images with the aim of improving diagnostic performance and providing increasing support to clinical decisions. In recent years, a number of studies have been published regarding different possible applications of radiomics in gynaecological imaging. Many fields have been explored, such as tumour diagnosis and staging, differentiation of histological subtypes, assessment of distant metastases, prediction of response to therapy, recurrence, and patients’ outcome. However, several studies are not robust, do not include validation cohorts, or lack reproducibility. On these bases, the purpose of this narrative review is to provide an overview of the most relevant studies in the literature on radiomics in gynaecological imaging. We focused on gynaecological malignancies, particularly endometrial, cervical, mesenchymal, and ovarian malignant pathologies.

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Development and validation of MRI-based radiomics model to predict recurrence risk in patients with endometrial cancer: a multicenter study

Cited by 7 publications

References 46 publications

Tumor Radiomics Features on Pretreatment CT to Predict Response to First-Line Chemotherapy in Thymic Carcinoma: A Multicenter Study

Tumor Radiomics Features on Pretreatment CT to Predict Response to First-Line Chemotherapy in Thymic Carcinoma: A Multicenter Study

Prediction of prostate cancer aggressiveness using magnetic resonance imaging radiomics: a dual-center study

Radiomics in Gynaecological Imaging: A State-of-the-Art Review

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