The Development and Validation of a CT-Based Radiomics Nomogram to Preoperatively Predict Lymph Node Metastasis in High-Grade Serous Ovarian Cancer

A clinicoradiological model based on clinical and CT features for preoperative prediction of histological classification in patients with epithelial ovarian cancers: a two-center study

OBJECTIVES

To develop and validate a clinicoradiological model integrating clinical and computed tomography (CT) features to preoperative predict histological classification in patients with epithelial ovarian cancers (EOCs).

METHODS

This retrospective study included 470 patients who were pathologically proven EOCs and performed by contrast enhanced CT before treatment from center I (training cohort, N = 329; internal test cohort, N = 141) and 83 EOC patients who were included as an external test cohort from center II. The univariate analysis and multivariate logistic regression analysis were used to select significant clinical and CT features. The significant clinical model was developed based on clinical characteristics. The significant radiological model was established by CT features. The significant clinical and CT features were used to construct the clinicoradiological model. Model performances were evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, the Brier score and decision curve analysis (DCA). The AUCs were compared by net reclassification index (NRI) and integrated discrimination improvement (IDI).

RESULTS

The significant clinical and CT parameters including age, transverse diameter, morphology, margin, ascites and lymphadenopathy were incorporated to build the clinicoradioligical model. The clinicoradiological model showed relatively satisfactory discrimination between type I and type II EOCs with the AUC of 0.841 (95% confidence interval [CI] 0.797-0.886), 0.874 (95% CI 0.811-0.937) and 0.826 (95% CI 0.729-0.923) in the training, internal and external test cohorts, respectively. The NRI and IDI showed the clinicoradiological model significantly performed than those of the clinical model (all P < 0.05). No statistical significance was found between radiological and clinicoradiological model. The clinicoradiological model demonstrated optimal classification accuracy and clinical application value.

CONCLUSION

The easily accessible nomogram based on the clinicoradiologic model showed favorable performance in distinguishing between type I and type II EOCs and could therefore be used to improve the clinical management of EOC patients.

CT-based radiomics predicts HRD score and HRD status in patients with ovarian cancer

Introduction

This study predicted HRD score and status based on intra- and peritumoral radiomics in patients with ovarian cancer (OC) for better guiding the use of PARPi in clinical.

Methods

A total of 106 and 95 patients with OC were included between January 2022 and November 2023 for predicting HRD score and status, respectively. Radiomics features were extracted and quantitatively analyzed from intra- and peri-tumor regions in the CT image. Radiomics signatures (RSs) were built based on features from intra- and peri-tumor regions for predicting HRD score and status alone or in combination. Subject working characteristics (ROC) area under the curve (AUC), sensitivity (SEN), and specificity (SPE) were calculated as comparative metrics.

Results

For predicting HRD score, 4 and 2 features were selected as the most important predictors from the intra- and peritumoral regions, respectively. For predicting HRD status, 4 features from the intratumoral region and 2 from the peritumoral region were identified as the most important predictors. Radiomics nomograms created by combining RSs and important clinical factors showed good predictive results with AUCs of 0.852 (95% confidence interval [CI]: 0.765-0.938, SEN = 0.907, SPE = 0.655) and 0.781 (95% CI: 0.621-0.941, SEN = 0.688, SPE = 0.833) in the training and validation cohort for predicting HRD score, respectively; with AUCs of 0.874 (95% CI: 0.790-0.957, SEN = 0.765, SPE = 0.867) and 0.824 (95% CI: 0.663-0.985, SEN = 0.762, SPE = 0.800) in the training and validation cohort for predicting HRD status, respectively.

Discussion

Calibration curves and decision curve analysis (DCA) confirmed potential clinical usefulness of our nomograms. Our findings suggest that radiomics features derived from the CT image of OC have the potential to predict HRD score and status, and the developed nomograms can enrich the range of applicable population of PARPi, prolong progression-free survival and provide personalized treatment for OC patients.

Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer

Ovarian cancer (OC) remains one of the most lethal gynecological malignancies globally. Despite the implementation of various medical imaging approaches for OC screening, achieving accurate differential diagnosis of ovarian tumors continues to pose significant challenges due to variability in image performance, resulting in a lack of objectivity that relies heavily on the expertise of medical professionals. This challenge can be addressed through the emergence and advancement of radiomics, which enables high-throughput extraction of valuable information from conventional medical images. Furthermore, radiomics can integrate with genomics, a novel approach termed radiogenomics, which allows for a more comprehensive, precise, and personalized assessment of tumor biological features. In this review, we present an extensive overview of the application of radiomics and radiogenomics in diagnosing and predicting ovarian tumors. The findings indicate that artificial intelligence methods based on imaging can accurately differentiate between benign and malignant ovarian tumors, as well as classify their subtypes. Moreover, these methods are effective in forecasting survival rates, treatment outcomes, metastasis risk, and recurrence for patients with OC. It is anticipated that these advancements will function as decision-support tools for managing OC while contributing to the advancement of precision medicine.

CT-based radiomic analysis for categorization of ovarian sex cord-stromal tumors and epithelial ovarian cancers

PURPOSE

To evaluate the diagnostic potential of radiomic analyses based on machine learning that rely on contrast-enhanced computerized tomography (CT) for categorizing ovarian sex cord-stromal tumors (SCSTs) and epithelial ovarian cancers (EOCs).

METHODS

We included a total of 225 patients with 230 tumors, who were randomly divided into training and test cohorts with a ratio of 8:2. Radiomic features were extracted from each tumor and dimensionally reduced using LASSO. We used univariate and multivariate analyses to identify independent predictors from clinical features and conventional CT parameters. Clinic-radiological model, radiomics model and mixed model were constructed respectively. We evaluated model performance via analysis of the receiver operating characteristic (ROC) curve and area under ROC curves (AUCs), and compared it across models using the Delong test.

RESULTS

We selected a support vector machine as the best classifier. Both radiomic and mixed model achieved good classification accuracy with AUC values of 0.923/0.930 in the training cohort, and 0.879/0.909 in the test cohort. The mixed model performed significantly better than the model based on clinical radiological information, with AUC values of 0.930 versus 0.826 (p = 0.000) in the training cohort and 0.905 versus 0.788 (p = 0.042) in the test cohort.

CONCLUSION

Radiomic analysis based on CT images is a reliable and noninvasive tool for identifying SCSTs and EOCs, outperforming experience radiologists.

Advances in ovarian cancer radiomics: a bibliometric analysis from 2010 to 2024

Objective

Ovarian cancer, a leading cause of death among gynecological malignancies, often eludes early detection, leading to diagnoses at advanced stages. The objective of this bibliometric analysis is to map the landscape of ovarian cancer radiomics research from 2010 to 2024, emphasizing its growth, global contributions, and the impact of emerging technologies on early diagnosis and treatment strategies.

Methods

A comprehensive search was conducted using the Web of Science Core Collection (WoSCC), focusing on publications related to radiomics and ovarian cancer within the specified period. Analytical tools such as VOSviewer and CiteSpace were employed to visualize trends, collaborations, and key contributions, while the R programming environment offered further statistical insights.

Results

From the initial dataset, 149 articles were selected, showing a significant increase in research output, especially in the years 2021-2023. The analysis revealed a dominant contribution from China, with significant inputs from England. Major institutional contributors included the University of Cambridge and GE Healthcare. 'Frontiers in Oncology' emerged as a crucial journal in the field, according to Bradford's Law. Keyword analysis highlighted the focus on advanced imaging techniques and machine learning.

Conclusions

The steady growth in ovarian cancer radiomics research reflects its critical role in advancing diagnostic and prognostic methodologies, underscoring the potential of radiomics in the shift towards personalized medicine. Despite some methodological challenges, the field's dynamic evolution suggests a promising future for radiomics in enhancing the accuracy of ovarian cancer diagnosis and treatment, contributing to improved patient care and outcomes.

Predicting CD27 expression and clinical prognosis in serous ovarian cancer using CT-based radiomics

BACKGROUND

This study aimed to develop and evaluate radiomics models to predict CD27 expression and clinical prognosis before surgery in patients with serous ovarian cancer (SOC).

METHODS

We used transcriptome sequencing data and contrast-enhanced computed tomography images of patients with SOC from The Cancer Genome Atlas (n = 339) and The Cancer Imaging Archive (n = 57) and evaluated the clinical significance and prognostic value of CD27 expression. Radiomics features were selected to create a recursive feature elimination-logistic regression (RFE-LR) model and a least absolute shrinkage and selection operator logistic regression (LASSO-LR) model for CD27 expression prediction.

RESULTS

CD27 expression was upregulated in tumor samples, and a high expression level was determined to be an independent protective factor for survival. A set of three and six radiomics features were extracted to develop RFE-LR and LASSO-LR radiomics models, respectively. Both models demonstrated good calibration and clinical benefits, as determined by the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. The LASSO-LR model performed better than the RFE-LR model, owing to the area under the curve (AUC) values of the ROC curves (0.829 vs. 0.736). Furthermore, the AUC value of the radiomics score that predicted the overall survival of patients with SOC diagnosed after 60 months was 0.788 using the LASSO-LR model.

CONCLUSION

The radiomics models we developed are promising noninvasive tools for predicting CD27 expression status and SOC prognosis. The LASSO-LR model is highly recommended for evaluating the preoperative risk stratification for SOCs in clinical applications.

Development and validation of radiomics nomogram for metastatic status of epithelial ovarian cancer

To develop and validate an enhanced CT-based radiomics nomogram for evaluating preoperative metastasis risk of epithelial ovarian cancer (EOC). One hundred and nine patients with histologically confirmed EOC were retrospectively enrolled. The volume of interest (VOI) was delineated in preoperative enhanced CT images, and 851 radiomics features were extracted. The radiomics features were selected by the least absolute shrinkage and selection operator (LASSO), and the rad-score was calculated using the formula of the radiomics label. A clinical model, radiomics model, and combined model were constructed using the logistic regression classification algorithm. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the diagnostic performance of the models. Seventy-five patients (68.8%) were histologically confirmed to have metastasis. Eleven optimal radiomics features were retained by the LASSO algorithm to develop the radiomic model. The combined model for evaluating metastasis of EOC achieved area under the curve (AUC) values of 0.929 (95% CI 0.8593-0.9996) in the training cohort and 0.909 (95% CI 0.7921-1.0000) in the test cohort. To facilitate clinical use, a radiomic nomogram was built by combining the clinical characteristics with rad-score. The DCA indicated that the nomogram had the most significant net benefit when the threshold probability exceeded 15%, surpassing the benefits of both the treat-all and treat-none strategies. Compared with clinical model and radiomics model, the radiomics nomogram has the best diagnostic performance in evaluating EOC metastasis. The nomogram is a useful and convenient tool for clinical doctors to develop personalized treatment plans for EOC patients.

Contrast-enhanced CT radiomics for preoperative prediction of stage in epithelial ovarian cancer: a multicenter study

BACKGROUND

Preoperative prediction of International Federation of Gynecology and Obstetrics (FIGO) stage in patients with epithelial ovarian cancer (EOC) is crucial for determining appropriate treatment strategy. This study aimed to explore the value of contrast-enhanced CT (CECT) radiomics in predicting preoperative FIGO staging of EOC, and to validate the stability of the model through an independent external dataset.

METHODS

A total of 201 EOC patients from three centers, divided into a training cohort (n = 106), internal (n = 46) and external (n = 49) validation cohorts. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for screening radiomics features. Five machine learning algorithms, namely logistic regression, support vector machine, random forest, light gradient boosting machine (LightGBM), and decision tree, were utilized in developing the radiomics model. The optimal performing algorithm was selected to establish the radiomics model, clinical model, and the combined model. The diagnostic performances of the models were evaluated through receiver operating characteristic analysis, and the comparison of the area under curves (AUCs) were conducted using the Delong test or F-test.

RESULTS

Seven optimal radiomics features were retained by the LASSO algorithm. The five radiomics models demonstrate that the LightGBM model exhibits notable prediction efficiency and robustness, as evidenced by AUCs of 0.83 in the training cohort, 0.80 in the internal validation cohort, and 0.68 in the external validation cohort. The multivariate logistic regression analysis indicated that carcinoma antigen 125 and tumor location were identified as independent predictors for the FIGO staging of EOC. The combined model exhibited best diagnostic efficiency, with AUCs of 0.95 in the training cohort, 0.83 in the internal validation cohort, and 0.79 in the external validation cohort. The F-test indicated that the combined model exhibited a significantly superior AUC value compared to the radiomics model in the training cohort (P < 0.001).

CONCLUSIONS

The combined model integrating clinical characteristics and radiomics features shows potential as a non-invasive adjunctive diagnostic modality for preoperative evaluation of the FIGO staging status of EOC, thereby facilitating clinical decision-making and enhancing patient outcomes.

Development and validation of an ultrasound‑based radiomics nomogram to predict lymph node status in patients with high-grade serous ovarian cancer: a retrospective analysis

BACKGROUND

Despite advances in medical imaging technology, the accurate preoperative prediction of lymph node status remains challenging in ovarian cancer. This retrospective study aimed to investigate the feasibility of using ultrasound-based radiomics combined with preoperative clinical characteristics to predict lymph node metastasis (LNM) in patients with high-grade serous ovarian cancer (HGSOC).

RESULTS

Patients with 401 HGSOC lesions from two institutions were enrolled: institution 1 for the training cohort (n = 322) and institution 2 for the external test cohort (n = 79). Radiomics features were extracted from the three preoperative ultrasound images of each lesion. During feature selection, primary screening was first performed using the sample variance F-value, followed by recursive feature elimination (RFE) to filter out the 12 most significant features for predicting LNM. The radscore derived from these 12 radiomic features and three clinical characteristics were used to construct a combined model and nomogram to predict LNM, and subsequent 10-fold cross-validation was performed. In the test phase, the three models were tested with external test cohort. The radiomics model had an area under the curve (AUC) of 0.899 (95% confidence interval [CI]: 0.864-0.933) in the training cohort and 0.855 (95%CI: 0.774-0.935) in the test cohort. The combined model showed good calibration and discrimination in the training cohort (AUC = 0.930) and test cohort (AUC = 0.881), which were superior to those of the radiomic and clinical models alone.

CONCLUSIONS

The nomogram consisting of the radscore and preoperative clinical characteristics showed good diagnostic performance in predicting LNM in patients with HGSOC. It may be used as a noninvasive method for assessing the lymph node status in these patients.

Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics

Objective

High-grade serous ovarian cancer (HGSOC) has the highest mortality rate among female reproductive system tumors. Accurate preoperative assessment is crucial for treatment planning. This study aims to develop multitask prediction models for HGSOC using radiomics analysis based on preoperative CT images.

Methods

This study enrolled 112 patients diagnosed with HGSOC. Laboratory findings, including serum levels of CA125, HE-4, and NLR, were collected. Radiomic features were extracted from manually delineated ROI on CT images by two radiologists. Classification models were developed using selected optimal feature sets to predict R0 resection, lymph node invasion, and distant metastasis status. Model evaluation was conducted by quantifying receiver operating curves (ROC), calculating the area under the curve (AUC), De Long's test.

Results

The radiomics models applied to CT images demonstrated superior performance in the testing set compared to the clinical models. The area under the curve (AUC) values for the combined model in predicting R0 resection were 0.913 and 0.881 in the training and testing datasets, respectively. De Long's test indicated significant differences between the combined and clinical models in the testing set (p = 0.003). For predicting lymph node invasion, the AUCs of the combined model were 0.868 and 0.800 in the training and testing datasets, respectively. The results also revealed significant differences between the combined and clinical models in the testing set (p = 0.002). The combined model for predicting distant metastasis achieved AUCs of 0.872 and 0.796 in the training and test datasets, respectively. The combined model displayed excellent agreement between observed and predicted results in predicting R0 resection, while the radiomics model demonstrated better calibration than both the clinical model and combined model in predicting lymph node invasion and distant metastasis. The decision curve analysis (DCA) for predicting R0 resection favored the combined model over both the clinical and radiomics models, whereas for predicting lymph node invasion and distant metastasis, DCA favored the radiomics model over both the clinical model and combined model.

Conclusion

The identified radiomics signature holds potential value in preoperatively evaluating the R0, lymph node invasion and distant metastasis in patients with HGSC. The radiomics nomogram demonstrated the incremental value of clinical predictors for surgical outcome and metastasis estimation.

Diagnostic value of a CT-based radiomics nomogram for discrimination of benign and early stage malignant ovarian tumors

BACKGROUND

This study aimed to identify the diagnostic value of models constructed using computed tomography-based radiomics features for discrimination of benign and early stage malignant ovarian tumors.

METHODS

The imaging and clinicopathological data of 197 cases of benign and early stage malignant ovarian tumors (FIGO stage I/II), were retrospectively analyzed. The patients were randomly assigned into training data set and validation data set. Radiomics features were extracted from images of plain computed tomography scan and contrast-enhanced computed tomography scan, were then screened in the training data set, and a radiomics model was constructed. Multivariate logistic regression analysis was used to construct a radiomic nomogram, containing the traditional diagnostic model and the radiomics model. Moreover, the decision curve analysis was used to assess the clinical application value of the radiomics nomogram.

RESULTS

Six textural features with the greatest diagnostic efficiency were finally screened. The value of the area under the receiver operating characteristic curve showed that the radiomics nomogram was superior to the traditional diagnostic model and the radiomics model (P < 0.05) in the training data set. In the validation data set, the radiomics nomogram was superior to the traditional diagnostic model (P < 0.05), but there was no statistically significant difference compared to the radiomics model (P > 0.05). The calibration curve and the Hosmer-Lemeshow test revealed that the three models all had a great degree of fit (All P > 0.05). The results of decision curve analysis indicated that utilization of the radiomics nomogram to distinguish benign and early stage malignant ovarian tumors had a greater clinical application value when the risk threshold was 0.4-1.0.

CONCLUSIONS

The computed tomography-based radiomics nomogram could be a non-invasive and reliable imaging method to discriminate benign and early stage malignant ovarian tumors.

Nomogram for predicting lymph node metastasis in patients with ovarian cancer using ultrasonography: a multicenter retrospective study

BACKGROUND

Ovarian cancer is a common cancer among women globally, and the assessment of lymph node metastasis plays a crucial role in the treatment of this malignancy. The primary objective of our study was to identify the risk factors associated with lymph node metastasis in patients with ovarian cancer and develop a predictive model to aid in the selection of the appropriate surgical procedure and treatment strategy.

METHODS

We conducted a retrospective analysis of data from patients with ovarian cancer across three different medical centers between April 2014 and August 2022. Logistic regression analysis was employed to establish a prediction model for lymph node metastasis in patients with ovarian cancer. We evaluated the performance of the model using receiver operating characteristic (ROC) curves, calibration plots, and decision analysis curves.

RESULTS

Our analysis revealed that among the 368 patients in the training set, 101 patients (27.4%) had undergone lymph node metastasis. Maximum tumor diameter, multifocal tumor, and Ki67 level were identified as independent risk factors for lymph node metastasis. The area under the curve (AUC) of the ROC curve in the training set was 0.837 (95% confidence interval [CI]: 0.792-0.881); in the validation set this value was 0.814 (95% CI: 0.744-0.884). Calibration plots and decision analysis curves revealed good calibration and clinical application value.

CONCLUSIONS

We successfully developed a model for predicting lymph node metastasis in patients with ovarian cancer, based on ultrasound examination results and clinical data. Our model accurately identified patients at high risk of lymph node metastasis and may guide the selection of appropriate treatment strategies. This model has the potential to significantly enhance the precision and efficacy of clinical management in patients with ovarian cancer.

Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

The objective of this study was to evaluate the discriminative capabilities of radiomics signatures derived from three distinct machine learning algorithms and to identify a robust radiomics signature capable of predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy in patients diagnosed with locally advanced rectal cancer (LARC). In a retrospective study, 211 LARC patients were consecutively enrolled and divided into a training cohort (n = 148) and a validation cohort (n = 63). From pretreatment contrast-enhanced planning CT images, a total of 851 radiomics features were extracted. Feature selection and radiomics score (Radscore) construction were performed using three different machine learning methods: least absolute shrinkage and selection operator (LASSO), random forest (RF) and support vector machine (SVM). The SVM-derived Radscore demonstrated a strong correlation with the pCR status, yielding area under the receiver operating characteristic curves (AUCs) of 0.880 and 0.830 in the training and validation cohorts, respectively, outperforming the RF and LASSO methods. Based on this, a nomogram was developed by combining the SVM-based Radscore with clinical indicators to predict pCR after neoadjuvant chemoradiotherapy. The nomogram exhibited superior predictive power, achieving AUCs of 0.910 and 0.866 in the training and validation cohorts, respectively. Calibration curves and decision curve analyses confirmed its appropriateness. The SVM-based Radscore demonstrated promising performance in predicting pCR for LARC patients. The machine learning-driven nomogram, which integrates the Radscore and clinical indicators, represents a valuable tool for predicting pCR in LARC patients.

Predictive value of PET metabolic parameters for occult lymph node metastases in PET/CT defined node-negative patients with advanced epithelial ovarian cancer

Accurate lymph node metastasis (LNM) prediction is crucial for patients with advanced epithelial ovarian cancer (AEOC) since it guides the decisions about lymphadenectomy. Previous studies have shown that occult lymph node metastasis (OLNM) is common in AEOC. The objective of our study is to quantitatively assess the probability of occult lymph node metastasis defined by 18F-Fluorodeoxyglucose PET/CT in AEOC and explore relationship between OLNM and PET metabolic parameters. The patients with pathologically confirmed AEOC who underwent PET/CT for preoperative staging at our institute were reviewed. Univariate and multivariate analysis were performed to evaluate the predictive value of PET/CT-related metabolic parameters for OLNM. The result of our study showed metastatic TLG index had a better diagnostic performance than other PET/CT-related metabolic parameters. Two variables were independently and significantly associated with OLNM in multivariate analysis: metastatic TLG index and primary tumor location. The logistic model combining metastatic TLG index, primary tumor location, and CA125 might be a promising tool to effectively predict the individualized possibility of OLNM for AEOC patients.

Clinical significance of enlarged cardiophrenic lymph nodes by CT in advanced ovarian cancer

AIM

This study aims to assess the clinical influence of enlarged cardiophrenic lymph nodes (CPLN) on staging computed tomography (CT) among patients with advanced ovarian cancer.

METHODS

This retrospective cohort study included 320 patients with advanced epithelial ovarian cancer who underwent staging CT from May 2008 to January 2019. The CPLN diameter was the average of two radiologists' measurements. Enlarged CPLN was defined as a short-axis diameter of ≥5 mm. Clinical and imaging findings, management decisions, and progression-free survival(PFS) were compared between patients with and without enlarged CPLN.

RESULTS

Enlarged CPLN was found in 129 (40.3%) patients, which was significantly associated with more pelvic peritoneal carcinomatosis (odds ratio [OR]: 6.61 with 95% confidence interval [CI]: 1.51-28.99), and involved the greater omentum (OR: 6.41, 95% CI: 3.05-13.46), spleen capsule nodules (OR: 2.83, 95% CI: 1.58-5.06), and liver capsule nodules (OR: 2.55, 95% CI: 1.57-4.17). The optimal cytoreduction rates did not differ between patients with and without enlarged CPLN (p = 0.656). The presence of enlarged CPLN had a significant negative influence on PFS (median PFS, 23.5 vs. 80.6 months, respectively, CPLN ≥5 mm versus <5 mm; p = 0.023) in patients with no RD after primary debulking surgery, but no adverse effect on PFS among patients with RD (median PFS, 28.0 vs. 24.4 months, respectively, CPLN ≥5 mm versus <5 mm; p = 0.359). However, enlarged CPLN on staging CT did not affect PFS in patients treated with neoadjuvant chemotherapy, with (median PFS, 22.4 vs. 23.6 months, respectively, CPLN ≥5 mm versus <5 mm; p = 0.360) or without RD (median PFS, 17.7 vs. 23.3 months, respectively, CPLN ≥5 mm versus <5 mm; p = 0.400). The enlarged CPLN showed a decreased trend in 81.6% (n = 80) of the patients with enlarged CPLN. No significant difference was found in PFS (p = 0.562) between patients with decreased and increased in the size of CPLN.

CONCLUSIONS

Enlarged CPLN on staging CT is associated with more abdominal disease but is not reliable in predicting complete resection. Enlarged CPLN awareness is necessary for patients with a primary chance of complete resection of abdominal disease.

Radiomics and Radiogenomics of Ovarian Cancer

Ovarian cancer, one of the deadliest gynecologic malignancies, is characterized by high intra- and inter-site genomic and phenotypic heterogeneity. The traditional information provided by the conventional interpretation of diagnostic imaging studies cannot adequately represent this heterogeneity. Radiomics analyses can capture the complex patterns related to the microstructure of the tissues and provide quantitative information about them. This review outlines how radiomics and its integration with other quantitative biological information, like genomics and proteomics, can impact the clinical management of ovarian cancer.

CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer

OBJECTIVE

We aimed to evaluate the prognostic value of C-C motif chemokine receptor type 5 (CCR5) expression level for patients with ovarian cancer and to establish a radiomics model that can predict CCR5 expression level using The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) database.

METHODS

A total of 343 cases of ovarian cancer from the TCGA were used for the gene-based prognostic analysis. Fifty seven cases had preoperative computed tomography (CT) images stored in TCIA with genomic data in TCGA were used for radiomics feature extraction and model construction. 89 cases with both TCGA and TCIA clinical data were used for radiomics model evaluation. After feature extraction, a radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. A prognostic scoring system incorporating radiomics signature based on CCR5 expression level and clinicopathologic risk factors was proposed for survival prediction.

RESULTS

CCR5 was identified as a differentially expressed prognosis-related gene in tumor and normal sample, which were involved in the regulation of immune response and tumor invasion and metastasis. Four optimal radiomics features were selected to predict overall survival. The performance of the radiomics model for predicting the CCR5 expression level with 10-fold cross- validation achieved Area Under Curve (AUCs) of 0.770 and of 0.726, respectively, in the training and validation sets. A predictive nomogram was generated based on the total risk score of each patient, the AUCs of the time-dependent receiver operating characteristic (ROC) curve of the model was 0.8, 0.673 and 0.792 for 1-year, 3-year and 5-year, respectively. Along with clinical features, important imaging biomarkers could improve the overall survival accuracy of the prediction model.

CONCLUSION

The expression levels of CCR5 can affect the prognosis of patients with ovarian cancer. CT-based radiomics could serve as a new tool for prognosis prediction.

Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma

IMPORTANCE

Epithelial ovarian carcinoma is heterogeneous and classified according to the World Health Organization Tumour Classification, which is based on histologic features and molecular alterations. Preoperative prediction of the histologic subtypes could aid in clinical management and disease prognostication.

OBJECTIVE

To assess the value of radiomics based on contrast-enhanced computed tomography (CT) in differentiating histologic subtypes of epithelial ovarian carcinoma in multicenter data sets.

DESIGN, SETTING, AND PARTICIPANTS

In this diagnostic study, 665 patients with histologically confirmed epithelial ovarian carcinoma were retrospectively recruited from 4 centers (Hong Kong, Guangdong Province of China, and Seoul, South Korea) between January 1, 2012, and February 28, 2022. The patients were randomly divided into a training cohort (n = 532) and a testing cohort (n = 133) with a ratio of 8:2. This process was repeated 100 times. Tumor segmentation was manually delineated on each section of contrast-enhanced CT images to encompass the entire tumor. The Mann-Whitney U test and voted least absolute shrinkage and selection operator were performed for feature reduction and selection. Selected features were used to build the logistic regression model for differentiating high-grade serous carcinoma and non-high-grade serous carcinoma.

EXPOSURES

Contrast-enhanced CT-based radiomics.

MAIN OUTCOMES AND MEASURES

Intraobserver and interobserver reproducibility of tumor segmentation were measured by Dice similarity coefficients. The diagnostic efficiency of the model was assessed by receiver operating characteristic curve and area under the curve.

RESULTS

In this study, 665 female patients (mean [SD] age, 53.6 [10.9] years) with epithelial ovarian carcinoma were enrolled and analyzed. The Dice similarity coefficients of intraobserver and interobserver were all greater than 0.80. Twenty radiomic features were selected for modeling. The areas under the curve of the logistic regression model in differentiating high-grade serous carcinoma and non-high-grade serous carcinoma were 0.837 (95% CI, 0.835-0.838) for the training cohort and 0.836 (95% CI, 0.833-0.840) for the testing cohort.

CONCLUSIONS AND RELEVANCE

In this diagnostic study, radiomic features extracted from contrast-enhanced CT were useful in the classification of histologic subtypes in epithelial ovarian carcinoma. Intraobserver and interobserver reproducibility of tumor segmentation was excellent. The proposed logistic regression model offered excellent discriminative ability among histologic subtypes.

CT-Based Radiomics and Deep Learning for BRCA Mutation and Progression-Free Survival Prediction in Ovarian Cancer Using a Multicentric Dataset

PURPOSE

Build predictive radiomic models for early relapse and BRCA mutation based on a multicentric database of high-grade serous ovarian cancer (HGSOC) and validate them in a test set coming from different institutions.

METHODS

Preoperative CTs of patients with HGSOC treated at four referral centers were retrospectively acquired and manually segmented. Hand-crafted features and deep radiomics features were extracted respectively by dedicated software (MODDICOM) and a dedicated convolutional neural network (CNN). Features were selected with and without prior harmonization (ComBat harmonization), and models were built using different machine learning algorithms, including clinical variables.

RESULTS

We included 218 patients. Radiomic models showed low performance in predicting both BRCA mutation (AUC in test set between 0.46 and 0.59) and 1-year relapse (AUC in test set between 0.46 and 0.56); deep learning models demonstrated similar results (AUC in the test of 0.48 for BRCA and 0.50 for relapse). The inclusion of clinical variables improved the performance of the radiomic models to predict BRCA mutation (AUC in the test set of 0.74).

CONCLUSIONS

In our multicentric dataset, representative of a real-life clinical scenario, we could not find a good radiomic predicting model for PFS and BRCA mutational status, with both traditional radiomics and deep learning, but the combination of clinical and radiomic models improved model performance for the prediction of BRCA mutation. These findings highlight the need for standardization through the whole radiomic pipelines and robust multicentric external validations of results.