Association between the quantitative characteristics of dual-energy spectral CT and cytoreduction surgery outcome in patients with advanced epithelial ovarian cancers: A prospective observational study.

This study aimed to explore the association between the quantitative characteristics of dual-energy spectral CT and cytoreduction surgery outcome in patients with advanced epithelial ovarian carcinoma (EOC). In this prospective observational study, patients with advanced EOC (federation of gynecology and obstetrics stage III-IV) treated in the Department of Gynecological Oncology at our Hospital between June 2021 and March 2022 were enrolled. All participants underwent dual-energy spectral computed tomography (DECT) scanning 2 weeks before cytoreductive surgery. The quantitative data included peritoneal cancer index (PCI) determined by DECT, CT value at 70 keV, normalized iodine concentration, normalized water concentration, effective atomic number (effective-Z), and slopes of the spectral attenuation curves (slope λ Hounsfield unit). Fifty-five participants were included. The patients were 57.2 ±â€…9.8 years of age, and 72.7% were menopausal. The maximal diameter of tumors was 8.6 (range, 2.9-19.7) cm, and 76.4% were high-grade serous carcinomas. Optimal cytoreduction was achieved in 43 patients (78.2%). Compared with the optimal cytoreductive group, the suboptimal cytoreductive group showed a higher PCI (median, 21 vs 6, P < .001), higher 70 keV CT value (69.5 ±â€…16.6 vs 57.1 ±â€…13.0, P = .008), and higher slope λ Hounsfield unit (1.89 ±â€…0.66 vs 1.39 ±â€…0.60, P = .015). The multivariable analysis showed that the PCI (OR = 1.74, 95%CI: 1.24-2.44, P = .001) and 70 keV CT value (OR = 1.07, 95%CI: 1.01-1.13, P = .023) were independently associated with a suboptimal cytoreductive surgery. The area under the receiver operating characteristics curve of PCI and 70 keV CT value was 0.903 (95%CI: 0.805-1.000, P = .000) and 0.740 (95%CI: 0.581-0.899, P = .012), respectively. High PCI and 70 keV CT value are independently associated with suboptimal cytoreductive surgery in patients with advanced EOC. The PCI determined by DECT might be a better predictor for suboptimal cytoreduction.

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.

Initial Experience with the Radiotracer 18F-Fluciclovine PET/CT in Ovarian Cancer.

OBJECTIVE: Early and accurate staging of ovarian cancer is paramount to disease survival. Conventional imaging including FDG PET/CT are limited in the evaluation of small metastatic lesions. 18F-Fluciclovine has minimal urine and bowel excretion allowing optimal visualization of the abdomen and pelvis. This study examines 18F-fluciclovine uptake in known primary and recurrent ovarian cancer. METHODS: Seven patients with a confirmed diagnosis of epithelial ovarian cancer underwent 18F-fluciclovine PET/CT imaging. Forty-one (41) lesions were identified with 18F-fluciclovine and confirmed to be true positive (n = 41). We aim to explore if 18F-fluciclovine uptake in ovarian lesions were greater than background uptake of bone marrow, blood pool, and bladder. Quantification analysis was performed to determine max and mean standard uptake values (SUVmax and SUVmean) of known and suspected lesions compared to SUVmean uptake of background structures. RESULTS: 18F-Fluciclovine demonstrated 100% sensitivity (41/41) for uptake in known ovarian lesions. The average SUVmax (±SD) uptake of known ovarian lesions was 5.9 (±2.6) and 5.1 (±2.0) on early and delayed images, respectively. The average tumor SUVmax to SUVmean of background (±SD) (T:B) ratios on early and delay were 1.9 (±0.8), 2.1 (±0.9) for marrow; 3.8 (±1.8), 3.4 (±1.5) for aorta; and 8.4 (±4.3), 1.5 (±1.7) for bladder, respectively. CONCLUSION: 18F-Fluciclovine uptake in malignant ovarian lesions was above background levels suggesting its feasibility in the imaging of ovarian cancer. Due to increasing tracer washout via the urinary bladder over time, early imaging at 4 min post injection is favorable.

Visceral obesity determined by CT as a predictor of short-term postoperative complications in patients with ovarian cancer.

OBJECTIVE: To explore the association between visceral obesity and short-term postoperative complications in patients with advanced ovarian cancer undergoing cytoreductive surgery. METHODS: The medical records of patients with advanced epithelial ovarian cancer were reviewed. The visceral fat area, subcutaneous fat area and total fat area at the L3/4 level were measured on a preoperative single-slice CT scan. The receiver operating characteristic (ROC) curve was used to calculate the optimal cutoff value for the visceral fat area. The relationship between the visceral fat area and the characteristics of ovarian cancer patients were analyzed. Univariable and multivariable logistic regression analyses were performed to investigate relationship between perioperative characteristics and short-term complications. RESULTS: According to the ROC curve, the best cutoff value of the VFA was 93 cm2. Of the 130 patients, 53.8% (70/130) had visceral obesity. Patients with visceral obesity were older than those with nonvisceral obesity (58.4 years old vs. 52.1 years old, p < 0.001). The proportion of patients with hypertension was higher (35.7 vs. 13.3%, p = 0.003). The total fat area and subcutaneous fat area were larger in patients with visceral obesity (294.3 ± 75.5 vs. 176.2 ± 68.7, p < 0.001; 158.9 ± 54.7 vs. 121.7 ± 52.6, p < 0.001). Compared with patients in the nonvisceral obese group, patients in the visceral obese group were more likely to have postoperative fever (21/70 30.0% vs. 8/60 1.25%, p = 0.023), leading to a longer length of hospital stay (21 days vs. 17 days, p = 0.009). The time from surgery to adjuvant chemotherapy for patients with visceral obesity was shorter (24 days vs. 19 days, p = 0.037). Multivariate analysis showed that visceral obesity (OR = 6.451, p < 0.001) and operation time (OR = 1.006, p < 0.001) were independent predictors of postoperative complications. CONCLUSION: Visceral obesity is an important risk factor for short-term postoperative complications in patients with advanced ovarian cancer undergoing cytoreductive surgery.

Role of magnetic resonance imaging to differentiate between borderline and malignant serous epithelial ovarian tumors.

PURPOSE: We aimed to differentiate serous borderline ovarian tumors (SBOT) from serous epithelial ovarian carcinomas (SEOC) using morphological and functional MRI findings, to improve the patient management. METHOD: We retrospectively investigated 24 ovarian lesions diagnosed with SBOT and 64 ovarian lesions diagnosed with SEOC. Additional to the demographic and morphological findings T2W signal intensity ratio, mean apparent diffusion coefficient (ADCmean) and total apparent diffusion coefficient (ADCtotal) values were analyzed and compared between two groups. RESULTS: Bilaterality, pelvic free fluid presence, serum CA-125 level (U/mL), presence of pelvic peritoneal implant were in favor of SEOC. Lower maximum size of solid component and solid size to maximum size ratio, dominantly cystic and solid-cystic appearance, exophytic growth pattern, presence of papiller projection and papillary architecture and internal branching pattern, higher T2W signal intensity ratio, ADCmean and ADCtotal values were in favor of SBOT. CONCLUSION: Our study revealed that morphological and functional imaging findings were valuable in differentiating BSOT from SEOC.

Associating Peritoneal Metastasis With T2-Weighted MRI Images in Epithelial Ovarian Cancer Using Deep Learning and Radiomics: A Multicenter Study.

BACKGROUND: The preoperative diagnosis of peritoneal metastasis (PM) in epithelial ovarian cancer (EOC) is challenging and can impact clinical decision-making. PURPOSE: To investigate the performance of T2 -weighted (T2W) MRI-based deep learning (DL) and radiomics methods for PM evaluation in EOC patients. STUDY TYPE: Retrospective. POPULATION: Four hundred seventy-nine patients from five centers, including one training set (N = 297 [mean, 54.87 years]), one internal validation set (N = 75 [mean, 56.67 years]), and two external validation sets (N = 53 [mean, 55.58 years] and N = 54 [mean, 58.22 years]). FIELD STRENGTH/SEQUENCE: 1.5 or 3 T/fat-suppression T2W fast or turbo spin-echo sequence. ASSESSMENT: ResNet-50 was used as the architecture of DL. The largest orthogonal slices of the tumor area, radiomics features, and clinical characteristics were used to construct the DL, radiomics, and clinical models, respectively. The three models were combined using decision-level fusion to create an ensemble model. Diagnostic performances of radiologists and radiology residents with and without model assistance were evaluated. STATISTICAL TESTS: Receiver operating characteristic analysis was used to assess the performances of models. The McNemar test was used to compare sensitivity and specificity. A two-tailed P < 0.05 was considered significant. RESULTS: The ensemble model had the best AUCs, outperforming the DL model (0.844 vs. 0.743, internal validation set; 0.859 vs. 0.737, external validation set I) and clinical model (0.872 vs. 0.730, external validation set II). After model assistance, all readers had significantly improved sensitivity, especially for those with less experience (junior radiologist1, from 0.639 to 0.820; junior radiologist2, from 0.689 to 0.803; resident1, from 0.623 to 0.803; resident2, from 0.541 to 0.738). One resident also had significantly improved specificity (from 0.633 to 0.789). DATA CONCLUSIONS: T2W MRI-based DL and radiomics approaches have the potential to preoperatively predict PM in EOC patients and assist in clinical decision-making. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

[68Ga]Ga-FAPI-04 PET/CT in the evaluation of epithelial ovarian cancer: comparison with [18F]F-FDG PET/CT.

PURPOSE: To compare the efficacy of [68Ga]Ga-FAPI-04 PET/CT in primary or recurrent tumors and metastatic lesions of epithelial ovarian cancer (EOC) with that of fluorine-18 fluorodeoxyglucose ([18F]F-FDG) PET/CT. METHODS: Forty-nine patients (median age, 57 years; IQR, 51-66 years) with histologically proven primary or relapsed EOC were enrolled. Participants underwent [18F]F-FDG and [68Ga]Ga-FAPI-04 PET/CT. The detection rate, diagnostic accuracy, semiquantitative parameters, tumor staging, and clinical management of the tracers were compared. The diagnostic performance of [18F]F-FDG and [68Ga]Ga-FAPI-04 PET/CT was evaluated and compared using surgical pathology. Differences between methods regarding the peritoneal cancer index (PCI) using preoperative imaging, surgical PCI, and tumor markers (CA125, HE4) were also assessed regarding peritoneal metastases. RESULTS: Among the 49 patients, 28 had primary EOC; 21 had relapsed EOC. [68Ga]Ga-FAPI-04 PET/CT outperformed [18F]F-FDG PET/CT in detecting peritoneal metastases (96.8% vs. 83.0%; p < 0.001), retroperitoneal (99.5% vs. 91.4%; p < 0.001), and supradiaphragmatic lymph node metastases (100% vs. 80.4%; p < 0.001). Compared with [18F]F-FDG, [68Ga]Ga-FAPI-04 showed higher SUVmax for peritoneal metastases (17.31 vs. 13.68; p = 0.026) and retroperitoneal (8.72 vs. 6.56; p < 0.001) and supradiaphragmatic lymph node metastases (6.39 vs. 4.20; p < 0.001). Moreover, [68Ga]Ga-FAPI-04 PET/CT showed higher sensitivity compared with [18F]F-FDG PET/CT for detecting metastatic lymph nodes (80.6% vs. 61.3%; p = 0.031) and peritoneal metastases (97.5% vs. 75.9%; p < 0.001), using surgical pathology as the gold standard. Compared with [18F]F-FDG PET/CT, [68Ga]Ga-FAPI-04 PET/CT led to an upgrade in 14.3% and 33.3% of treatment-naive and relapse participants, resulting in management changes in 10.7% and 19.0% of the patients, respectively. The median PCIFAPI scores were significantly higher than PCIFDG (15 vs. 11; p < 0.001) and positively correlated with CA125 and HE4 levels and surgical PCI. CONCLUSION: [68Ga]Ga-FAPI-04 PET/CT achieved higher sensitivity than [18F]F-FDG PET/CT in the detection and diagnosis of lymph node and peritoneal metastases, suggesting advantages regarding the preoperative staging of patients with EOC and, thereby, improving treatment decision-making. TRIAL REGISTRATION: NCT05034146. Registered February 23, 2021.

Predicting resectable disease in relapsed epithelial ovarian cancer by using whole-body diffusion-weighted MRI.

OBJECTIVE: To determine the diagnostic value of whole-body diffusion-weighted magnetic resonance imaging (WB-DWI/MRI) to predict resectable disease at the time of secondary cytoreductive surgery for relapsed epithelial ovarian cancer with a platinum-free interval of at least 6 months. METHODS: A retrospective cohort study between January 2012 and December 2021 in a tertiary referral hospital. Inclusion criteria were: (a) first recurrence of epithelial ovarian cancer; (b) platinum-free interval of ≥6 months; (c) intent to perform secondary cytoreductive surgery with complete macroscopic resection; and (d) WB-DWI/MRI was performed.Diagnostic tests of WB-DWI/MRI for predicting complete resection during secondary cytoreductive surgery are calculated as well as the progression-free and overall survival of the patients with a WB-DWI/MRI scan that showed resectable disease or not. RESULTS: In total, 238 patients could be identified, of whom 123 (51.7%) underwent secondary cytoreductive surgery. WB-DWI/MRI predicted resectable disease with a sensitivity of 93.6% (95% confidence interval [CI] 87.3% to 96.9%), specificity of 93.0% (95% CI 87.3% to 96.3%), and an accuracy of 93.3% (95% CI 89.3% to 96.1%). The positive predictive value was 91.9% (95% CI 85.3% to 95.7%).Prediction of resectable disease by WB-DWI/MRI correlated with improved progression-free survival (median 19 months vs 9 months; hazard ratio [HR] for progression 0.36; 95% CI 0.26 to 0.50) and overall survival (median 75 months vs 28 months; HR for death 0.33; 95% CI 0.23 to 0.47). CONCLUSION: WB-DWI/MRI accurately predicts resectable disease in patients with a platinum-free interval of ≥6 months at the time of secondary cytoreductive surgery and could be of complementary value to the currently used models.

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.

Dynamic contrast-enhanced MRI to characterize angiogenesis in primary epithelial ovarian cancer: An exploratory study.

PURPOSE: Angiogenesis is essential for tumor growth. Currently, there are no established imaging biomarkers to show angiogenesis in tumor tissue. The aim of this prospective study was to evaluate whether semiquantitative and pharmacokinetic DCE-MRI perfusion parameters could be used to assess angiogenesis in epithelial ovarian cancer (EOC). METHOD: We enrolled 38 patients with primary EOC treated in 2011-2014. DCE-MRI was performed with a 3.0 T imaging system before the surgical treatment. Two different sizes of ROI were used to evaluate semiquantitative and pharmacokinetic DCE perfusion parameters: a large ROI (L-ROI) covering the whole primary lesion on one plane and a small ROI (S-ROI) covering a small solid, highly enhancing focus. Tissue samples from tumors were collected during the surgery. Immunohistochemistry was used to measure the expression of vascular endothelial growth factor (VEGF), its receptors (VEGFRs) and to analyse microvascular density (MVD) and the number of microvessels. RESULTS: VEGF expression correlated inversely with Ktrans (L-ROI, r = -0.395 (p = 0.009), S-ROI, r = -0.390, (p = 0.010)), Ve (L-ROI, r = -0.395 (p = 0.009), S-ROI, r = -0.412 (p = 0.006)) and Vp (L-ROI, r = -0.388 (p = 0.011), S-ROI, r = -0.339 (p = 0.028)) values in EOC. Higher VEGFR-2 correlated with lower DCE parameters Ktrans (L-ROI, r = -0.311 (p = 0.040), S-ROI, r = -0.337 (p = 0.025)) and Ve (L-ROI, r = -0.305 (p = 0.044), S-ROI, r = -0.355 (p = 0.018)). We also found that MVD and the number of microvessels correlated positively with AUC, Peak and WashIn values. CONCLUSIONS: We observed that several DCE-MRI parameters correlated with VEGF and VEGFR-2 expression and MVD. Thus, both semiquantitative and pharmacokinetic perfusion parameters of DCE-MRI represent promising tools for the assessment of angiogenesis in EOC.

Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer.

OBJECTIVE: To develop and validate a model that can preoperatively identify the ovarian clear cell carcinoma (OCCC) subtype in epithelial ovarian cancer (EOC) using CT imaging radiomics and clinical data. MATERIAL AND METHODS: We retrospectively analyzed data from 282 patients with EOC (training set = 225, testing set = 57) who underwent pre-surgery CT examinations. Patients were categorized into OCCC or other EOC subtypes based on postoperative pathology. Seven clinical characteristics (age, cancer antigen [CA]-125, CA-199, endometriosis, venous thromboembolism, hypercalcemia, stage) were collected. Primary tumors were manually delineated on portal venous-phase images, and 1218 radiomic features were extracted. The F-test-based feature selection method and logistic regression algorithm were used to build the radiomic signature, clinical model, and integrated model. To explore the effects of integrated model-assisted diagnosis, five radiologists independently interpreted images in the testing set and reevaluated cases two weeks later with knowledge of the integrated model's output. The diagnostic performances of the predictive models, radiologists, and radiologists aided by the integrated model were evaluated. RESULTS: The integrated model containing the radiomic signature (constructed by four wavelet radiomic features) and three clinical characteristics (CA-125, endometriosis, and hypercalcinemia), showed better diagnostic performance (AUC = 0.863 [0.762-0.964]) than the clinical model (AUC = 0.792 [0.630-0.953], p = 0.295) and the radiomic signature alone (AUC = 0.781 [0.636-0.926], p = 0.185). The diagnostic sensitivities of the radiologists were significantly improved when using the integrated model (p = 0.023-0.041), while the specificities and accuracies were maintained (p = 0.074-1.000). CONCLUSION: Our integrated model shows great potential to facilitate the early identification of the OCCC subtype in EOC, which may enhance subtype-specific therapy and clinical management.

Diffusion-weighted magnetic resonance imaging for the pre-operative evaluation of epithelial ovarian cancer patients.

OBJECTIVE: To investigate the value diffusion-weighted magnetic resonance imaging (DWI/MR) in the selection of ovarian cancer patients suitable for primary debulking surgery. METHODS: Patients with suspected ovarian cancer who underwent pre-operative DWI/MR were enrolled between April 2020 and March 2022. All participants received preoperative clinic-radiological assessment according to the Suidan criteria for R0 resection with a predictive score. Data for patients with primary debulking surgery were prospectively recorded. The diagnostic value was calculated with ROC curves, and the cut-off value for the predictive score was also explored. RESULTS: 80 patients with primary debulking surgery were included in the final analysis. The majority (97.5%) of patients were at advanced stage (III-IV), and 90.0% of patients had high-grade serous ovarian histology. 46 (57.5%) patients had no residual disease (R0), and 27 (33.8%) patients had optimal debulking surgery with zzmacroscopic disease less than or equal to 1 cm (R1). Patients with BRCA1 mutation had lower R0 resection rate, higher R1 resection rate compared with wild-type patients (42.9% vs 63.0%, 50.0% vs 29.6%, respectively). The median (range) predictive score was 4 (0-13), and the AUC for R0 resection was 0.742 (0.632-0.853). The R0 rates for patients with predictive score 0-2, 3-5, and ≥ 6 were 77.8%, 62.5% and 23.8%, respectively. CONCLUSION: DWI/MR was a sufficient technique for pre-operative evaluation of ovarian cancer. Patients with predictive score 0-5 were suitable for primary debulking surgery at our institution.

Computed tomography-based radiomics machine learning classifiers to differentiate type I and type II epithelial ovarian cancers.

OBJECTIVES: To compare computed tomography (CT)-based radiomics for preoperatively differentiating type I and II epithelial ovarian cancers (EOCs) using different machine learning classifiers and to construct and validate the best diagnostic model. METHODS: A total of 470 patients with EOCs were included retrospectively. Patients were divided into a training dataset (N = 329) and a test dataset (N = 141). A total of 1316 radiomics features were extracted from the portal venous phase of contrast-enhanced CT images for each patient, followed by dimension reduction of the features. The support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), naïve Bayes (NB), logistic regression (LR), and eXtreme Gradient Boosting (XGBoost) classifiers were trained to obtain the radiomics signatures. The performance of each radiomics signature was evaluated and compared by the area under the receiver operating characteristic curve (AUC) and relative standard deviation (RSD). The best radiomics signature was selected and combined with clinical and radiological features to establish a combined model. The diagnostic value of the combined model was assessed. RESULTS: The LR-based radiomics signature performed well in the test dataset, with an AUC of 0.879 and an accuracy of 0.773. The combined model performed best in both the training and test datasets, with AUCs of 0.900 and 0.934 and accuracies of 0.848 and 0.823, respectively. CONCLUSION: The combined model showed the best diagnostic performance for distinguishing between type I and II EOCs preoperatively. Therefore, it can be a useful tool for clinical individualized EOC classification. KEY POINTS: • Radiomics features extracted from computed tomography (CT) could be used to differentiate type I and II epithelial ovarian cancers (EOCs). • Machine learning can improve the performance of differentiating type I and II EOCs. • The combined model exhibited the best diagnostic capability over the other models in both the training and test datasets.

Updates on fluorescent probes and open-field imaging methods for fluorescence-guided cytoreductive surgery for epithelial ovarian cancer: A review.

Fluorescence-guided surgery has emerged as a promising imaging technique for real-time intraoperative tumour delineation and visualisation of submillimetre tumour masses in cytoreductive surgery for epithelial ovarian cancer (EOC). Researchers have developed several EOC-targeted fluorescent probes, most of which are currently in the preclinical stage. Interestingly, imaging devices designed for open surgery are proof of concept. This review summarises the recent advances in EOC-targeted fluorescent probes and open-field fluorescence imaging strategies and discusses the challenges and potential solutions for clinical translation.

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.

A pre-operative radiological score to predict lymph node metastasis in advanced epithelial ovarian cancer.

BACKGROUND: Lymphadenectomy is part of cytoreductive surgery for patients with advanced epithelial ovarian cancer (AEOC) in case of abnormal lymph nodes before and during surgery. The aim of this study was to develop and validate a pre-operative radiological score to predict pelvic and/or para-aortic lymph node metastasis (LNM) in patients with AEOC undergoing cytoreductive surgery. METHODS: We conducted a multicentre retrospective study. The construction sample was composed of 53 patients operated within two referral centers. The validation sample was composed of 39 patients operated in a third referral center. The score was built with a logistic regression model with internal validation by bootstrap. RESULTS: Two variables were associated with the prediction of pelvic and/or para-aortic LNM at computerized tomography (CT) and/or positron emission tomography (PET/CT): "para-aortic lymph node involvement" (adjusted diagnostic odds ratio) (aDOR) = 8.77 95CI [1.42-54.09], p = 0.02) and "colon involvement" (aDOR = 7.97 95CI [1.28-49.58], p = 0.03). Bootstrap procedure showed that the model was stable. The 2-points LNM pre-operative radiological score was derived from these 2 radiological variables and a high-risk group was identified for a score ≥ 1: the probability of pelvic and/or para-aortic LNM was 76%, the specificity was 85.7% 95CI [67.3-96.0] and the positive likelihood ratio was 3.6 95CI [1.4-9.7]. In the validation sample, a score ≥ 1 had a specificity of 78.3% and a LR+ of 1.2. CONCLUSION: LNM pre-operative radiological score could help the surgeon's decision to perform pelvic and para-aortic lymphadenectomy in patients with AEOC undergoing cytoreductive surgery. TRIAL REGISTRATION: The research protocol was approved by the Ethics Committee for Research in Obstetrics and Gynecology (CEROG 2016-GYN 1003).

Ultrasound-based radiomics for predicting different pathological subtypes of epithelial ovarian cancer before surgery.

OBJECTIVE: To evaluate the value of ultrasound-based radiomics in the preoperative prediction of type I and type II epithelial ovarian cancer. METHODS: A total of 154 patients with epithelial ovarian cancer were enrolled retrospectively. There were 102 unilateral lesions and 52 bilateral lesions among a total of 206 lesions. The data for the 206 lesions were randomly divided into a training set (53 type I + 71 type II) and a test set (36 type I + 46 type II) by random sampling. ITK-SNAP software was used to manually outline the boundary of the tumor, that is, the region of interest, and 4976 features were extracted. The quantitative expression values of the radiomics features were normalized by the Z-score method, and the 7 features with the most differences were screened by using the Lasso regression tenfold cross-validation method. The radiomics model was established by logistic regression. The training set was used to construct the model, and the test set was used to evaluate the predictive efficiency of the model. On the basis of multifactor logistic regression analysis, combined with the radiomics score of each patient, a comprehensive prediction model was established, the nomogram was drawn, and the prediction effect was evaluated by analyzing the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve. RESULTS: The AUCs of the training set and test set in the radiomics model and comprehensive model were 0.817 and 0.731 and 0.982 and 0.886, respectively. The calibration curve showed that the two models were in good agreement. The clinical decision curve showed that both methods had good clinical practicability. CONCLUSION: The radiomics model based on ultrasound images has a good predictive effect for the preoperative differential diagnosis of type I and type II epithelial ovarian cancer. The comprehensive model has higher prediction efficiency.

A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients.

In a growing number of social and clinical scenarios, machine learning (ML) is emerging as a promising tool for implementing complex multi-parametric decision-making algorithms. Regarding ovarian cancer (OC), despite the standardization of features that can support the discrimination of ovarian masses into benign and malignant, there is a lack of accurate predictive modeling based on ultrasound (US) examination for progression-free survival (PFS). This retrospective observational study analyzed patients with epithelial ovarian cancer (EOC) who were followed in a tertiary center from 2018 to 2019. Demographic features, clinical characteristics, information about the surgery and post-surgery histopathology were collected. Additionally, we recorded data about US examinations according to the International Ovarian Tumor Analysis (IOTA) classification. Our study aimed to realize a tool to predict 12 month PFS in patients with OC based on a ML algorithm applied to gynecological ultrasound assessment. Proper feature selection was used to determine an attribute core set. Three different machine learning algorithms, namely Logistic Regression (LR), Random Forest (RFF), and K-nearest neighbors (KNN), were then trained and validated with five-fold cross-validation to predict 12 month PFS. Our analysis included n. 64 patients and 12 month PFS was achieved by 46/64 patients (71.9%). The attribute core set used to train machine learning algorithms included age, menopause, CA-125 value, histotype, FIGO stage and US characteristics, such as major lesion diameter, side, echogenicity, color score, major solid component diameter, presence of carcinosis. RFF showed the best performance (accuracy 93.7%, precision 90%, recall 90%, area under receiver operating characteristic curve (AUROC) 0.92). We developed an accurate ML model to predict 12 month PFS.

Nomogram based on ultrasound radiomics score and clinical variables for predicting histologic subtypes of epithelial ovarian cancer.

OBJECTIVE: Ovarian cancer is one of the most common causes of death in gynecological tumors, and its most common type is epithelial ovarian cancer (EOC). This study aimed to establish a radiomics signature based on ultrasound images to predict the histopathological types of EOC. METHODS: Overall, 265 patients with EOC who underwent preoperative ultrasonography and surgery were eligible. They were randomly sorted into two cohorts (training cohort: test cohort = 7:3). We outlined the region of interest of the tumor on the ultrasound images of the lesion. Then, the radiomics features were extracted. Clinical, Rad-score and combined models were constructed based on the least absolute shrinkage, selection operator, and logistic regression analysis. The performance of the models was evaluated using receiver operating characteristic curves and decision curve analysis (DCA). A nomogram was formulated based on the combined prediction model. RESULTS: The combined model had good performance in predicting EOC histopathological types, with an AUC of 0.83 (95% CI: 0.77-0.90) and 0.82 (95% CI: 0.71-0.93) in the training and test cohorts, respectively. The calibration curves showed that the nomogram estimation was consistent with the actual observations. DCA also verified the clinical value of the combined model. CONCLUSIONS: The combined model containing clinical and ultrasound radiomics features showed an excellent performance in predicting type I and type II EOC. ADVANCES IN KNOWLEDGE: This study presents the first application of ultrasound radiomics features to distinguish EOC histopathological types. The proposed clinical-radiomics nomogram could help gynecologists non-invasively identify EOC types before surgery.

Impact of no residual disease on postoperative computed tomography on survival in patients with optimally debulked advanced high-grade serous ovarian cancer during upfront surgery.

OBJECTIVE: We sought to investigate the impact of size of residual tumors as determined by postoperative computed tomography (CT) on survival of patients with advanced, high-grade serous ovarian carcinoma (HGSC) who achieved residual disease less than 1 cm after primary debulking surgery (PDS). METHODS: We collected data of patients with stage III HGSC who had residual tumor less than 1 cm after PDS between 2013 and 2018. Surgeon-assessed residual disease during surgery was defined as sR0 (no gross residual) or sR1 (gross residual <1 cm), and radiologist-assessed residual disease on postoperative CT was defined as rR0 (no evidence of disease) or rRany (existing residual disease). All patients were classified into the following groups: sR0/rR0, sR1/rR0, sR0/rRany, and sR1/rRany. RESULTS: A total of 436 patients was placed into the sR0/rR0 (n = 187, 42.9%), sR1/rR0 (n = 59, 13.5%), sR0/rRany (n = 79, 18.1%), or sR1/rRany group (n = 111, 25.5%). Discrepancies between surgical and radiological assessments were recorded for 176 patients (40.4%) including 38 cases of sR1/rRany group with discordant residual tumor location indicated between two methods. During multivariate analysis, patients with ascites on preoperative CT, sR0/rRany group inclusion, and sR1/rRany group inclusion showed unfavorable progression-free and overall survival. CONCLUSIONS: The incorporation of surgical and radiological evaluations for determining the size of residual tumors was more accurate than surgical evaluation only for predicting survival among patients with advanced ovarian cancer who underwent PDS to residual disease less than 1 cm.