Ultrasound evaluation of ovarian masses and assessment of the extension of ovarian malignancy

Bioconjugates for Cancer Prevention: Opportunities for Impact

Cancer prevention encompasses both screening strategies to find cancers early when they are likely to be most treatable and prevention and interception strategies to reduce the risk of developing cancers. Bioconjugates, here defined broadly as materials and molecules that have synthetic and biological components, have roles to play across the cancer-prevention spectrum. In particular, bioconjugates may be developed as affordable, accessible, and effective screening strategies or as novel vaccines and drugs to reduce one's risk of developing cancers. Developmental programs are available for taking novel technologies and evaluating them for clinical use in cancer screening and prevention. While a variety of different challenges exist in implementing cancer-prevention interventions, a thoughtful approach to bioconjugates could improve the delivery and acceptability of the interventions.

Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review

The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS-AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open-source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression-free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods.

Adnexal Mass Imaging: Contemporary Guidelines for Clinical Practice

Several recent guidelines have been published to improve accuracy and consistency of adnexal mass imaging interpretation and to guide management. Guidance from the American College of Radiology (ACR) Appropriateness Criteria establishes preferred adnexal imaging modalities and follow-up. Moreover, the ACR Ovarian-Adnexal Reporting Data System establishes a comprehensive, unified set of evidence-based guidelines for classification of adnexal masses by both ultrasound and MR imaging, communicating risk of malignancy to further guide management.

The physiological sonographic features of the ovary in healthy subjects: a joint systematic review and meta-analysis by the Italian Society of Gynecology and Obstetrics (SIGO) and the Italian Society of Endocrinology (SIE)

PURPOSE

There is a lack of uniformity in the definition of normal ovary ultrasound parameters. Our aim was to summarize and meta-analyze the evidence on the topic. Full-text English articles published through December 31, 2020 were retrieved via MEDLINE and Embase. Data available for meta-analysis included: ovarian follicular count, ovarian volume, and ovarian Pulsatility Index (PI) assessed by Doppler ultrasound.

METHODS

Cohort, cross-sectional, prospective studies with a single or double arm were considered eligible. Interventional studies were included when providing baseline data. Both studies on pre- and post-menopausal women were screened; however, data on menopausal women were not sufficient to perform a meta-analysis. Studies on pre-pubertal girls were considered separately. Eighty-one papers were included in the meta-analysis.

RESULTS

The mean ovarian volume was 6.11 [5.81-6.42] ml in healthy women in reproductive age (5.81-6.42) and 1.67 ml [1.02-2.32] in pre-pubertal girls. In reproductive age, the mean follicular count was 8.04 [7.26-8.82] when calculated in the whole ovary and 5.88 [5.20-6.56] in an ovarian section, and the mean ovarian PI was 1.86 [1.35-2.37]. Age and the frequency of the transducers partly modulated these values. In particular, the 25-30-year group showed the higher mean follicular count (9.27 [7.71-10.82]), followed by a progressive age-related reduction (5.67 [2.23-9.12] in fertile women > 35 years). A significant difference in follicular count was also found according to the transducer's upper MHz limit.

CONCLUSION

Our findings provide a significant input to improve the interpretation and diagnostic accuracy of ovarian ultrasound parameters in different physiological and pathological settings.

Performance of IOTA Simple Rules Risks, ADNEX Model, Subjective Assessment Compared to CA125 and HE4 with ROMA Algorithm in Discriminating between Benign, Borderline and Stage I Malignant Adnexal Lesions

BACKGROUND

Borderline ovarian tumors (BOTs) and early clinical stage malignant adnexal masses can make sonographic diagnosis challenging, while the clinical utility of tumor markers, e.g., CA125 and HE4, or the ROMA algorithm, remains controversial in such cases.

OBJECTIVE

To compare the IOTA group Simple Rules Risk (SRR), the ADNEX model and the subjective assessment (SA) with serum CA125, HE4 and the ROMA algorithm in the preoperative discrimination between benign tumors, BOTs and stage I malignant ovarian lesions (MOLs).

METHODS

A multicenter retrospective study was conducted with lesions classified prospectively using subjective assessment and tumor markers with the ROMA. The SRR assessment and ADNEX risk estimation were applied retrospectively. The sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR-) were calculated for all tests.

RESULTS

In total, 108 patients (the median age: 48 yrs, 44 postmenopausal) with 62 (79.6%) benign masses, 26 (24.1%) BOTs and 20 (18.5%) stage I MOLs were included. When comparing benign masses with combined BOTs and stage I MOLs, SA correctly identified 76% of benign masses, 69% of BOTs and 80% of stage I MOLs. Significant differences were found for the presence and size of the largest solid component (p = 0.0006), the number of papillary projections (p = 0.01), papillation contour (p = 0.008) and IOTA color score (p = 0.0009). The SRR and ADNEX models were characterized by the highest sensitivity (80% and 70%, respectively), whereas the highest specificity was found for SA (94%). The corresponding likelihood ratios were as follows: LR+ = 3.59 and LR- = 0.43 for the ADNEX; LR+ = 6.40 and LR- = 0.63 for SA and LR+ = 1.85 with LR- = 0.35 for the SRR. The sensitivity and specificity of the ROMA test were 50% and 85%, respectively, with LR+ = 3.44 and LR- = 0.58. Of all the tests, the ADNEX model had the highest diagnostic accuracy of 76%.

CONCLUSIONS

This study demonstrates the limited value of diagnostics based on CA125 and HE4 serum tumor markers and the ROMA algorithm as independent modalities for the detection of BOTs and early stage adnexal malignant tumors in women. SA and IOTA methods based on ultrasound examination may present superior value over tumor marker assessment.

Diffusion-weighted imaging-based radiomics in epithelial ovarian tumors: Assessment of histologic subtype

Background

Epithelial ovarian tumors (EOTs) are a group of heterogeneous neoplasms. It is importance to preoperatively differentiate the histologic subtypes of EOTs. Our study aims to investigate the potential of radiomics signatures based on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps for categorizing EOTs.

Methods

This retrospectively enrolled 146 EOTs patients [34 with borderline EOT(BEOT), 30 with type I and 82 with type II epithelial ovarian cancer (EOC)]. A total of 390 radiomics features were extracted from DWI and ADC maps. Subsequently, the LASSO algorithm was used to reduce the feature dimensions. A radiomics signature was established using multivariable logistic regression method with 3-fold cross-validation and repeated 50 times. Patients with bilateral lesions were included in the validation cohort and a heuristic selection method was established to select the tumor with maximum probability for final consideration. A nomogram incorporating the radiomics signature and clinical characteristics was also developed. Receiver operator characteristic, decision curve analysis (DCA), and net reclassification index (NRI) were applied to compare the diagnostic performance and clinical net benefit of predictive model.

Results

For distinguishing BEOT from EOC, the radiomics signature and nomogram showed more favorable discrimination than the clinical model (0.915 vs. 0.852 and 0.954 vs. 0.852, respectively) in the training cohort. In classifying early-stage type I and type II EOC, the radiomics signature exhibited superior diagnostic performance over the clinical model (AUC 0.905 vs. 0.735). The diagnostic efficacy of the nomogram was the same as that of the radiomics model with NRI value of -0.1591 (P = 0.7268). DCA also showed that the radiomics model and combined model had higher net benefits than the clinical model.

Conclusion

Radiomics analysis based on DWI, and ADC maps serve as an effective quantitative approach to categorize EOTs.

Diagnostic Value of Two-Dimensional Transvaginal Ultrasound Combined with Contrast-Enhanced Ultrasound in Ovarian Cancer

Objective

Explore the value of two-dimensional transvaginal ultrasound combined with contrast-enhanced ultrasound in the differential diagnosis of ovarian cancer, so as to provide the basis for clinical diagnosis and treatment of ovarian cancer.

Methods

A total of 100 suspected ovarian cancer patients who were admitted to our hospital from January 2019 to December 2021 were selected as the research subjects, including 62 ovarian cancer patients (ovarian cancer group) and 38 ovarian benign tumor patients (benign group). Two-dimensional vaginal ultrasound and contrast-enhanced ultrasound were performed in both groups. The differences in PI, RI, EDV, PSV, and VM parameters of the two groups as well as those of patients with ovarian cancer of different grades were compared. Record the contrast-enhanced ultrasound parameters such as AT, TTP and IMAX, and determine the diagnostic value.

Results

The PI and RI of the ovarian cancer group were lower than those of the benign ovarian tumor group, and the EDV, PSV and VM of the ovarian cancer group were higher than those of the benign ovarian tumor group (p < 0.05). The PI and RI of the patients in stage I–II of the ovarian cancer group were higher than those in stage III–IV, and the EDV, PSV and VM were lower than those in the patients in stage III–IV, with statistical significance (p < 0.05). The results of contrast-enhanced ultrasound showed that the AT and TTP values in the ovarian cancer group were significantly shorter than those in the benign group, and the peak intensity was significantly higher than that in the benign group, and the differences were statistically significant (p < 0.05). The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of two-dimensional ultrasound combined with contrast-enhanced ultrasound in the diagnosis of ovarian cancer were high, 95.16%(59/62), 86.84%(33/38), 92.19%(59/64), 91.67%(33/36) and 92.00%(92/100), respectively.

Conclusion

Contrast-enhanced ultrasound to some extent makes up for the deficiencies of conventional ultrasound, is helpful to detect early ovarian cancer, and can be used for the differential diagnosis of small ovarian tumors with difficult two-dimensional ultrasound diagnosis. Two-dimensional ultrasound combined with contrast-enhanced ultrasound can effectively improve the detection rate and differential diagnosis value of ovarian cancer, which is of great significance in the early diagnosis and differentiation of ovarian cancer.

Comparison of HE4, CA125, ROMA and CPH-I for Preoperative Assessment of Adnexal Tumors

(1) OBJECTIVE: To assess the performance of CA125, HE4, ROMA index and CPH-I index to preoperatively identify epithelial ovarian cancer (EOC) or metastatic cancer in the ovary (MCO). (2) METHODS: single center retrospective study, including women with a diagnosis of adnexal mass. We obtained the AUC, sensitivity, specificity and predictive values were of HE4, CA125, ROMA and CPH-I for the diagnosis of EOC and MCO. Subgroup analysis for women harboring adnexal masses with inconclusive diagnosis of malignancy by ultrasound features and Stage I EOC was performed. (3) RESULTS: 1071 patients were included, 852 (79.6%) presented benign/borderline tumors and 219 (20.4%) presented EOC/MCO. AUC for HE4 was higher than for CA125 (0.91 vs. 0.87). No differences were seen between AUC of ROMA and CPH-I, but they were both higher than HE4 AUC. None of the tumor markers alone achieved a sensitivity of 90%; HE4 was highly specific (93.5%). ROMA showed a sensitivity and specificity of 91.1% and 84.6% respectively, while CPH-I showed a sensitivity of 91.1% with 79.2% specificity. For patients with inconclusive diagnosis of malignancy by ultrasound features and with Stage I EOC, ROMA showed the best diagnostic performance (4) CONCLUSIONS: ROMA and CPH-I perform better than tumor markers alone to identify patients harboring EOC or MCO. They can be helpful to assess the risk of malignancy of adnexal masses, especially in cases where ultrasonographic diagnosis is challenging (stage I EOC, inconclusive diagnosis of malignancy by ultrasound features).