Radiomics: a Novel CT-Based Method of Predicting Postoperative Recurrence in Ovarian Cancer

Multiparametric MRI-based radiomics nomogram for differentiation of primary mucinous ovarian cancer from metastatic ovarian cancer

OBJECTIVE

To develop a multiparametric magnetic resonance imaging (mpMRI)-based radiomics nomogram and evaluate its performance in differentiating primary mucinous ovarian cancer (PMOC) from metastatic ovarian cancer (MOC).

METHODS

A total of 194 patients with PMOC (n = 72) and MOC (n = 122) confirmed by histology were randomly divided into the primary cohort (n = 137) and validation cohort (n = 57). Radiomics features were extracted from axial fat-saturated T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (CE-T1WI) sequences of each lesion. The effective features were selected by minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression to develop a radiomics model. Combined with clinical features, multivariate logistic regression analysis was employed to develop a radiomics nomogram. The efficiency of nomogram was evaluated using the receiver operating characteristic (ROC) curve analysis and compared using DeLong test. Finally, the goodness of fit and clinical benefit of nomogram were assessed by calibration curves and decision curve analysis, respectively.

RESULTS

The radiomics nomogram, by combining the mpMRI radiomics features with clinical features, yielded area under the curve (AUC) values of 0.931 and 0.934 in the primary and validation cohorts, respectively. The predictive performance of the radiomics nomogram was significantly superior to the radiomics model (0.931 vs. 0.870, P = 0.004; 0.934 vs. 0.844, P = 0.032), the clinical model (0.931 vs. 0.858, P = 0.005; 0.934 vs. 0.847, P = 0.030), and radiologists (all P < 0.05) in the primary and validation cohorts, respectively. The decision curve analysis revealed that the nomogram could provide higher net benefit to patients.

CONCLUSION

The mpMRI-based radiomics nomogram exhibited notable predictive performance in differentiating PMOC from MOC, emerging as a non-invasive preoperative imaging approach.

CT-Based Radiomics and Machine Learning for Differentiating Benign, Borderline, and Early-Stage Malignant Ovarian Tumors

To explore the value of CT-based radiomics model in the differential diagnosis of benign ovarian tumors (BeOTs), borderline ovarian tumors (BOTs), and early malignant ovarian tumors (eMOTs). The retrospective research was conducted with pathologically confirmed 258 ovarian tumor patients from January 2014 to February 2021. The patients were randomly allocated to a training cohort (n = 198) and a test cohort (n = 60). By providing a three-dimensional (3D) characterization of the volume of interest (VOI) at the maximum level of images, 4238 radiomic features were extracted from the VOI per patient. The Wilcoxon-Mann-Whitney (WMW) test, least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) were employed to select the radiomic features. Five machine learning (ML) algorithms were applied to construct three-class diagnostic models. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the radiomics models. The test cohort was used to verify the generalization ability of the radiomics models. The receiver-operating characteristic (ROC) was used to evaluate diagnostic performance of radiomics model. Global and discrimination performance of five models was evaluated by average area under the ROC curve (AUC). The average ROC indicated that random forest (RF) diagnostic model in training cohort demonstrated the best diagnostic performance (micro/macro average AUC, 0.98/0.99), which was then confirmed with by LOOCV (micro/macro average AUC, 0.89/0.88) and external validation (test cohort) (micro/macro average AUC, 0.81/0.79). Our proposed CT-based radiomics diagnostic models may effectively assist in preoperatively differentiating BeOTs, BOTs, and eMOTs.

Radiomics in the evaluation of ovarian masses - a systematic review

OBJECTIVES

The study aim was to conduct a systematic review of the literature reporting the application of radiomics to imaging techniques in patients with ovarian lesions.

METHODS

MEDLINE/PubMed, Web of Science, Scopus, EMBASE, Ovid and ClinicalTrials.gov were searched for relevant articles. Using PRISMA criteria, data were extracted from short-listed studies. Validity and bias were assessed independently by 2 researchers in consensus using the Quality in Prognosis Studies (QUIPS) tool. Radiomic Quality Score (RQS) was utilised to assess radiomic methodology.

RESULTS

After duplicate removal, 63 articles were identified, of which 33 were eligible. Fifteen assessed lesion classifications, 10 treatment outcomes, 5 outcome predictions, 2 metastatic disease predictions and 1 classification/outcome prediction. The sample size ranged from 28 to 501 patients. Twelve studies investigated CT, 11 MRI, 4 ultrasound and 1 FDG PET-CT. Twenty-three studies (70%) incorporated 3D segmentation. Various modelling methods were used, most commonly LASSO (least absolute shrinkage and selection operator) (10/33). Five studies (15%) compared radiomic models to radiologist interpretation, all demonstrating superior performance. Only 6 studies (18%) included external validation. Five studies (15%) had a low overall risk of bias, 9 (27%) moderate, and 19 (58%) high risk of bias. The highest RQS achieved was 61.1%, and the lowest was - 16.7%.

CONCLUSION

Radiomics has the potential as a clinical diagnostic tool in patients with ovarian masses and may allow better lesion stratification, guiding more personalised patient care in the future. Standardisation of the feature extraction methodology, larger and more diverse patient cohorts and real-world evaluation is required before clinical translation.

CLINICAL RELEVANCE STATEMENT

Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction. Modelling with larger cohorts and real-world evaluation is required before clinical translation.

KEY POINTS

• Radiomics is emerging as a tool for enhancing clinical decisions in patients with ovarian masses. • Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction. • Modelling with larger cohorts and real-world evaluation is required before clinical translation.

Intra- and peritumoral radiomics for predicting early recurrence in patients with high-grade serous ovarian cancer

PURPOSE

To explore values of intra- and peritumoral CT-based radiomics for predicting recurrence in high-grade serous ovarian cancer (HGSOC) patients.

METHODS

This study enrolled 110 HGSOC patients from our hospital between Aug 2017 and Apr 2021. All patients underwent contrast-enhanced CT scans before treatment. The least absolute shrinkage and selection operator (LASSO) regression was used to select radiomics features from intra- and peritumoral areas. Radiomics signatures were built based on selected features from Intra-RS, Peri-RS, and in Com-RS. A nomogram was constructed by combining radiomics signatures and clinical parameters with predictive potential. Receiver operating characteristics (ROC), calibration, and decision curve analyses (DCA) curves were used to evaluate performance of the nomogram.

RESULTS

The intra- and peritumoral combined Com-RS showed effective ability in predicting recurrent HGSOC in the training (AUCs, Intra-RS vs. Peri-RS vs. Com-RS, 0.861 vs. 0.836 vs. 899) and validation (AUCs, Intra-RS vs. Peri-RS vs. Com-RS, 0.788 vs. 0.762 vs. 815) cohort. The Federation of International of FIGO stage, menstruation, and location were found to be strongly associated with tumor recurrence. The nomogram has the best predictive ability in the training (AUCs, Com-RS vs. clinical model vs. nomogram, 0.899 vs. 0.648 vs. 0.901) and validation (AUCs, Com-RS vs. clinical model vs. nomogram, 0.815 vs. 0.666 vs. 0.818) cohort.

CONCLUSION

Our findings suggested values of intra- and peritumoral-based radiomics for predicting recurrent HGSOC. The constructed nomogram may be of importance in clinical application.

Radiomics and radiogenomics in ovarian cancer: a literature review

Ovarian cancer remains one of the most lethal gynecological cancers in the world despite extensive progress in the areas of chemotherapy and surgery. Many studies have postulated that this is because of the profound heterogeneity that underpins response to therapy and prognosis. Standard imaging evaluation using CT or MRI does not take into account this tumoral heterogeneity especially in advanced stages with peritoneal carcinomatosis. As such, newly emergent fields in the assessment of tumor heterogeneity have been proposed using radiomics to evaluate the whole tumor burden heterogeneity as opposed to single biopsy sampling. This review provides an overview of radiomics, radiogenomics, and proteomics and examines the use of these newly emergent fields in assessing tumor heterogeneity and its implications in ovarian cancer.

Radiomics Analysis of PET and CT Components of 18F-FDG PET/CT Imaging for Prediction of Progression-Free Survival in Advanced High-Grade Serous Ovarian Cancer

Objective

To investigate radiomics features extracted from PET and CT components of ¹⁸F-FDG PET/CT images integrating clinical factors and metabolic parameters of PET to predict progression-free survival (PFS) in advanced high-grade serous ovarian cancer (HGSOC).

Methods

A total of 261 patients were finally enrolled in this study and randomly divided into training (n=182) and validation cohorts (n=79). The data of clinical features and metabolic parameters of PET were reviewed from hospital information system(HIS). All volumes of interest (VOIs) of PET/CT images were semi-automatically segmented with a threshold of 42% of maximal standard uptake value (SUVmax) in PET images. A total of 1700 (850×2) radiomics features were separately extracted from PET and CT components of PET/CT images. Then two radiomics signatures (RSs) were constructed by the least absolute shrinkage and selection operator (LASSO) method. The RSs of PET (PET_RS) and CT components(CT_RS) were separately divided into low and high RS groups according to the optimum cutoff value. The potential associations between RSs with PFS were assessed in training and validation cohorts based on the Log-rank test. Clinical features and metabolic parameters of PET images (PET_MP) with P-value <0.05 in univariate and multivariate Cox regression were combined with PET_RS and CT_RS to develop prediction nomograms (Clinical, Clinical+ PET_MP, Clinical+ PET_RS, Clinical+ CT_RS, Clinical+ PET_MP + PET_RS, Clinical+ PET_MP + CT_RS) by using multivariate Cox regression. The concordance index (C-index), calibration curve, and net reclassification improvement (NRI) was applied to evaluate the predictive performance of nomograms in training and validation cohorts.

Results

In univariate Cox regression analysis, six clinical features were significantly associated with PFS. Ten PET radiomics features were selected by LASSO to construct PET_RS, and 1 CT radiomics features to construct CT_RS. PET_RS and CT_RS was significantly associated with PFS both in training (P <0.00 for both RSs) and validation cohorts (P=0.01 for both RSs). Because there was no PET_MP significantly associated with PFS in training cohorts. Only three models were constructed by 4 clinical features with P-value <0.05 in multivariate Cox regression and RSs (Clinical, Clinical+ PET_RS, Clinical+ CT_RS). Clinical+ PET_RS model showed higher prognostic performance than other models in training cohort (C-index=0.70, 95% CI 0.68-0.72) and validation cohort (C-index=0.70, 95% CI 0.66-0.74). Calibration curves of each model for prediction of 1-, 3-year PFS indicated Clinical +PET_RS model showed excellent agreements between estimated and the observed 1-, 3-outcomes. Compared to the basic clinical model, Clinical+ PET_MS model resulted in greater improvement in predictive performance in the validation cohort.

Conclusion

PET_RS can improve diagnostic accuracy and provide complementary prognostic information compared with the use of clinical factors alone or combined with CT_RS. The newly developed radiomics nomogram is an effective tool to predict PFS for patients with advanced HGSOC.