Comparison of the ADNEX and ROMA risk prediction models for the diagnosis of ovarian cancer: a multicentre external validation in patients who underwent surgery.

BACKGROUND: Several diagnostic prediction models to help clinicians discriminate between benign and malignant adnexal masses are available. This study is a head-to-head comparison of the performance of the Assessment of Different NEoplasias in the adneXa (ADNEX) model with that of the Risk of Ovarian Malignancy Algorithm (ROMA). METHODS: This is a retrospective study based on prospectively included consecutive women with an adnexal tumour scheduled for surgery at five oncology centres and one non-oncology centre in four countries between 2015 and 2019. The reference standard was histology. Model performance for ADNEX and ROMA was evaluated regarding discrimination, calibration, and clinical utility. RESULTS: The primary analysis included 894 patients, of whom 434 (49%) had a malignant tumour. The area under the receiver operating characteristic curve (AUC) was 0.92 (95% CI 0.88-0.95) for ADNEX with CA125, 0.90 (0.84-0.94) for ADNEX without CA125, and 0.85 (0.80-0.89) for ROMA. ROMA, and to a lesser extent ADNEX, underestimated the risk of malignancy. Clinical utility was highest for ADNEX. ROMA had no clinical utility at decision thresholds <27%. CONCLUSIONS: ADNEX had better ability to discriminate between benign and malignant adnexal tumours and higher clinical utility than ROMA. CLINICAL TRIAL REGISTRATION: clinicaltrials.gov NCT01698632 and NCT02847832.

Pan-Cancer Detection and Typing by Mining Patterns in Large Genome-Wide Cell-Free DNA Sequencing Datasets.

BACKGROUND: Cell-free DNA (cfDNA) analysis holds great promise for non-invasive cancer screening, diagnosis, and monitoring. We hypothesized that mining the patterns of cfDNA shallow whole-genome sequencing datasets from patients with cancer could improve cancer detection. METHODS: By applying unsupervised clustering and supervised machine learning on large cfDNA shallow whole-genome sequencing datasets from healthy individuals (n = 367) and patients with different hematological (n = 238) and solid malignancies (n = 320), we identified cfDNA signatures that enabled cancer detection and typing. RESULTS: Unsupervised clustering revealed cancer type-specific sub-grouping. Classification using a supervised machine learning model yielded accuracies of 96% and 65% in discriminating hematological and solid malignancies from healthy controls, respectively. The accuracy of disease type prediction was 85% and 70% for the hematological and solid cancers, respectively. The potential utility of managing a specific cancer was demonstrated by classifying benign from invasive and borderline adnexal masses with an area under the curve of 0.87 and 0.74, respectively. CONCLUSIONS: This approach provides a generic analytical strategy for non-invasive pan-cancer detection and cancer type prediction.

ESGO/ISUOG/IOTA/ESGE Consensus Statement on pre-operative diagnosis of ovarian tumors.

The European Society of Gynaecological Oncology (ESGO), the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG), the International Ovarian Tumour Analysis (IOTA) group, and the European Society for Gynaecological Endoscopy (ESGE) jointly developed clinically relevant and evidence-based statements on the pre-operative diagnosis of ovarian tumors, including imaging techniques, biomarkers, and prediction models. ESGO/ISUOG/IOTA/ESGE nominated a multidisciplinary international group, including expert practising clinicians and researchers who have demonstrated leadership and expertise in the pre-operative diagnosis of ovarian tumors and management of patients with ovarian cancer (19 experts across Europe). A patient representative was also included in the group. To ensure that the statements were evidence-based, the current literature was reviewed and critically appraised. Preliminary statements were drafted based on the review of the relevant literature. During a conference call, the whole group discussed each preliminary statement and a first round of voting was carried out. Statements were removed when a consensus among group members was not obtained. The voters had the opportunity to provide comments/suggestions with their votes. The statements were then revised accordingly. Another round of voting was carried out according to the same rules to allow the whole group to evaluate the revised version of the statements. The group achieved consensus on 18 statements. This Consensus Statement presents these ESGO/ISUOG/IOTA/ESGE statements on the pre-operative diagnosis of ovarian tumors and the assessment of carcinomatosis, together with a summary of the evidence supporting each statement.

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses.

Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features and their extraction methods. Several texture features have been recently applied in discriminating malignant and benign ovarian masses by analysing B-mode images from ultrasound scan of the ovary with different levels of performance. However, comparative performance evaluation of these reported features using common sets of clinically approved images is lacking. This paper presents an empirical evaluation of seven commonly used texture features (histograms, moments of histogram, local binary patterns [256-bin and 59-bin], histograms of oriented gradients, fractal dimensions, and Gabor filter), using a collection of 242 ultrasound scan images of ovarian masses of various pathological characteristics. The evaluation examines not only the effectiveness of classification schemes based on the individual texture features but also the effectiveness of various combinations of these schemes using the simple majority-rule decision level fusion. Trained support vector machine classifiers on the individual texture features without any specific pre-processing, achieve levels of accuracy between 75% and 85% where the seven moments and the 256-bin LBP are at the lower end while the Gabor filter is at the upper end. Combining the classification results of the top k (k = 3, 5, 7) best performing features further improve the overall accuracy to a level between 86% and 90%. These evaluation results demonstrate that each of the investigated image-based texture features provides informative support in distinguishing benign or malignant ovarian masses.

Intraoperative Ultrasound during Fertility-Sparing Surgery: A Systematic Review and Practical Applications.

Fertility-sparing surgery (FSS) is an established concept within operative gynaecology. Intraoperative ultrasound (IOUS) has the potential of assessing lesion margins, allowing complete resection with minimal damage to the surrounding healthy tissue and could potentially play a major role in FSS for benign or malignant gynaecological pathologies. In this paper, we review the current literature on the use of IOUS in gynaecological FSS. We also propose technical guidance on the IOUS during FSS. The findings of this review demonstrate that IOUS can assist in the safe resection of disease with high rates of completion, low rates of recurrence and without damage to the nearby healthy reproductive organs. Improved training in transvaginal ultrasonography and minimal access surgery are likely to facilitate the application of IOUS in FSS.

Risk of complications in patients with conservatively managed ovarian tumours (IOTA5): a 2-year interim analysis of a multicentre, prospective, cohort study.

BACKGROUND: Ovarian tumours are usually surgically removed because of the presumed risk of complications. Few large prospective studies on long-term follow-up of adnexal masses exist. We aimed to estimate the cumulative incidence of cyst complications and malignancy during the first 2 years of follow-up after adnexal masses have been classified as benign by use of ultrasonography. METHODS: In the international, prospective, cohort International Ovarian Tumor Analysis Phase 5 (IOTA5) study, patients aged 18 years or older with at least one adnexal mass who had been selected for surgery or conservative management after ultrasound assessment were recruited consecutively from 36 cancer and non-cancer centres in 14 countries. Follow-up of patients managed conservatively is ongoing at present. In this 2-year interim analysis, we analysed patients who were selected for conservative management of an adnexal mass judged to be benign on ultrasound on the basis of subjective assessment of ultrasound images. Conservative management included ultrasound and clinical follow-up at intervals of 3 months and 6 months, and then every 12 months thereafter. The main outcomes of this 2-year interim analysis were cumulative incidence of spontaneous resolution of the mass, torsion or cyst rupture, or borderline or invasive malignancy confirmed surgically in patients with a newly diagnosed adnexal mass. IOTA5 is registered with ClinicalTrials.gov, number NCT01698632, and the central Ethics Committee and the Belgian Federal Agency for Medicines and Health Products, number S51375/B32220095331, and is ongoing. FINDINGS: Between Jan 1, 2012, and March 1, 2015, 8519 patients were recruited to IOTA5. 3144 (37%) patients selected for conservative management were eligible for inclusion in our analysis, of whom 221 (7%) had no follow-up data and 336 (11%) were operated on before a planned follow-up scan was done. Of 2587 (82%) patients with follow-up data, 668 (26%) had a mass that was already in follow-up at recruitment, and 1919 (74%) presented with a new mass at recruitment (ie, not already in follow-up in the centre before recruitment). Median follow-up of patients with new masses was 27 months (IQR 14-38). The cumulative incidence of spontaneous resolution within 2 years of follow-up among those with a new mass at recruitment (n=1919) was 20·2% (95% CI 18·4-22·1), and of finding invasive malignancy at surgery was 0·4% (95% CI 0·1-0·6), 0·3% (<0·1-0·5) for a borderline tumour, 0·4% (0·1-0·7) for torsion, and 0·2% (<0·1-0·4) for cyst rupture. INTERPRETATION: Our results suggest that the risk of malignancy and acute complications is low if adnexal masses with benign ultrasound morphology are managed conservatively, which could be of value when counselling patients, and supports conservative management of adnexal masses classified as benign by use of ultrasound. FUNDING: Research Foundation Flanders, KU Leuven, Swedish Research Council.

Myeloid-derived suppressor cells at diagnosis may discriminate between benign and malignant ovarian tumors.

BACKGROUND: The behavior of the immune system as a driver in the progression of ovarian cancer has barely been studied. Our knowledge is mainly limited to the intra-tumoral adaptive immune system. Because of the widespread metastases of ovarian cancer, an assessment of the circulating immune system seems more accurate.To demonstrate the presence of immune cells in blood samples of patients with ovarian neoplasms. METHODS: In this exploratory prospective cohort study, peripheral blood mononuclear cells were collected at diagnosis from 143 women, including 62 patients with benign cysts, 13 with borderline tumor, 41 with invasive ovarian cancer, and 27 age-matched healthy controls. Immune profile analyses, based on the presence of CD4 (cluster of differentiation), CD8, natural killer cells, myeloid-derived suppressor cells, and regulatory T cells, were performed by fluorescence activated cell sorting. RESULTS: In a multivariable analysis, six immune cells (activated regulatory T cells, natural killer cells, myeloid-derived suppressor cells, monocytic myeloid-derived suppressor cells, exhausted monocytic myeloid-derived suppressor cells, and total myeloid cells) were selected as independent predictors of malignancy, with an optimism-corrected area under the receiver operating characteristic curve (AUC) of 0.858. In contrast, a profile based on CD8 and regulatory T cells, the current standard in ovarian cancer immunology, resulted in an AUC of 0.639. CONCLUSIONS: Our immune profile in blood suggests an involvement of innate immunosuppression driven by myeloid-derived suppressor cells in the development of ovarian cancer. This finding could contribute to clinical management of patients and in selection of immunotherapy.

Intra- and Inter-Rater Agreement Describing Myometrial Lesions Using Morphologic Uterus Sonographic Assessment: A Pilot Study.

OBJECTIVES: To evaluate the intra- and inter-rater agreement for myometrial lesions using Morphologic Uterus Sonographic Assessment terminology. METHODS: Thirteen raters with high (n = 6) or medium experience (n = 7) assessed 30 3-dimensional ultrasound clips with (n = 20) and without (n = 10) benign myometrial lesions. Myometrial lesions were reported as poorly or well defined and then systematically evaluated for the presence of individual features. The clips were blindly assessed twice (at a 2-month interval). Intra- and inter-rater agreements were calculated with κ statistics. RESULTS: The reporting of poorly defined lesions reached moderate intra-rater agreement (κ = 0.49 [high experience] and 0.47 [medium experience]) and poor inter-rater agreement (κ = 0.39 [high experience] and 0.25 [medium experience]). The reporting of well-defined lesions reached good to very good intra-rater agreement (κ = 0.73 [high experience] and 0.82 [medium experience]) and good inter-rater agreement (κ = 0.75 [high experience] and 0.63 [medium experience]). Most individual features associated with ill-defined lesions reached moderate intra- and inter-rater agreement among highly experienced raters (κ = 0.41-0.60). The least reproducible features were myometrial cysts, hyperechoic islands, subendometrial lines and buds, and translesional flow (κ = 0.11-0.34). Most individual features associated with well-defined lesions reached moderate to good intra- and inter-rater agreement among all observers (κ = 0.41-0.80). The least reproducible features were a serosal contour, asymmetry, a hyperechoic rim, and fan-shaped shadows (κ = 0.00-0.35). CONCLUSIONS: The reporting of well-defined lesions showed excellent agreement, whereas the agreement for poorly defined lesions was low, even among highly experienced raters. The agreement on identifying individual features varied, especially for features associated with ill-defined lesions. Guidelines on minimum requirements for features associated with ill-defined lesions to be interpreted as poorly defined lesions may improve agreement.

Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumor Analysis group.

BACKGROUND: Accurate methods to preoperatively characterize adnexal tumors are pivotal for optimal patient management. A recent metaanalysis concluded that the International Ovarian Tumor Analysis algorithms such as the Simple Rules are the best approaches to preoperatively classify adnexal masses as benign or malignant. OBJECTIVE: We sought to develop and validate a model to predict the risk of malignancy in adnexal masses using the ultrasound features in the Simple Rules. STUDY DESIGN: This was an international cross-sectional cohort study involving 22 oncology centers, referral centers for ultrasonography, and general hospitals. We included consecutive patients with an adnexal tumor who underwent a standardized transvaginal ultrasound examination and were selected for surgery. Data on 5020 patients were recorded in 3 phases from 2002 through 2012. The 5 Simple Rules features indicative of a benign tumor (B-features) and the 5 features indicative of malignancy (M-features) are based on the presence of ascites, tumor morphology, and degree of vascularity at ultrasonography. Gold standard was the histopathologic diagnosis of the adnexal mass (pathologist blinded to ultrasound findings). Logistic regression analysis was used to estimate the risk of malignancy based on the 10 ultrasound features and type of center. The diagnostic performance was evaluated by area under the receiver operating characteristic curve, sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), positive predictive value (PPV), negative predictive value (NPV), and calibration curves. RESULTS: Data on 4848 patients were analyzed. The malignancy rate was 43% (1402/3263) in oncology centers and 17% (263/1585) in other centers. The area under the receiver operating characteristic curve on validation data was very similar in oncology centers (0.917; 95% confidence interval, 0.901-0.931) and other centers (0.916; 95% confidence interval, 0.873-0.945). Risk estimates showed good calibration. In all, 23% of patients in the validation data set had a very low estimated risk (<1%) and 48% had a high estimated risk (≥30%). For the 1% risk cutoff, sensitivity was 99.7%, specificity 33.7%, LR+ 1.5, LR- 0.010, PPV 44.8%, and NPV 98.9%. For the 30% risk cutoff, sensitivity was 89.0%, specificity 84.7%, LR+ 5.8, LR- 0.13, PPV 75.4%, and NPV 93.9%. CONCLUSION: Quantification of the risk of malignancy based on the Simple Rules has good diagnostic performance both in oncology centers and other centers. A simple classification based on these risk estimates may form the basis of a clinical management system. Patients with a high risk may benefit from surgery by a gynecological oncologist, while patients with a lower risk may be managed locally.

Machine learning and radiomics for segmentation and classification of adnexal masses on ultrasound.

Ultrasound-based models exist to support the classification of adnexal masses but are subjective and rely upon ultrasound expertise. We aimed to develop an end-to-end machine learning (ML) model capable of automating the classification of adnexal masses. In this retrospective study, transvaginal ultrasound scan images with linked diagnoses (ultrasound subjective assessment or histology) were extracted and segmented from Imperial College Healthcare, UK (ICH development dataset; n = 577 masses; 1444 images) and Morgagni-Pierantoni Hospital, Italy (MPH external dataset; n = 184 masses; 476 images). A segmentation and classification model was developed using convolutional neural networks and traditional radiomics features. Dice surface coefficient (DICE) was used to measure segmentation performance and area under the ROC curve (AUC), F1-score and recall for classification performance. The ICH and MPH datasets had a median age of 45 (IQR 35-60) and 48 (IQR 38-57) years old and consisted of 23.1% and 31.5% malignant cases, respectively. The best segmentation model achieved a DICE score of 0.85 ± 0.01, 0.88 ± 0.01 and 0.85 ± 0.01 in the ICH training, ICH validation and MPH test sets. The best classification model achieved a recall of 1.00 and F1-score of 0.88 (AUC:0.93), 0.94 (AUC:0.89) and 0.83 (AUC:0.90) in the ICH training, ICH validation and MPH test sets, respectively. We have developed an end-to-end radiomics-based model capable of adnexal mass segmentation and classification, with a comparable predictive performance (AUC 0.90) to the published performance of expert subjective assessment (gold standard), and current risk models. Further prospective evaluation of the classification performance of this ML model against existing methods is required.

External Validation of the Ovarian-Adnexal Reporting and Data System (O-RADS) Lexicon and the International Ovarian Tumor Analysis 2-Step Strategy to Stratify Ovarian Tumors Into O-RADS Risk Groups.

Importance: Correct diagnosis of ovarian cancer results in better prognosis. Adnexal lesions can be stratified into the Ovarian-Adnexal Reporting and Data System (O-RADS) risk of malignancy categories with either the O-RADS lexicon, proposed by the American College of Radiology, or the International Ovarian Tumor Analysis (IOTA) 2-step strategy. Objective: To investigate the diagnostic performance of the O-RADS lexicon and the IOTA 2-step strategy. Design, Setting, and Participants: Retrospective external diagnostic validation study based on interim data of IOTA5, a prospective international multicenter cohort study, in 36 oncology referral centers or other types of centers. A total of 8519 consecutive adult patients presenting with an adnexal mass between January 1, 2012, and March 1, 2015, and treated either with surgery or conservatively were included in this diagnostic study. Twenty-five patients were excluded for withdrawal of consent, 2777 were excluded from 19 centers that did not meet predefined data quality criteria, and 812 were excluded because they were already in follow-up at recruitment. The analysis included 4905 patients with a newly detected adnexal mass in 17 centers that met predefined data quality criteria. Data were analyzed from January 31 to March 1, 2022. Exposures: Stratification into O-RADS categories (malignancy risk <1%, 1% to <10%, 10% to <50%, and ≥50%). For the IOTA 2-step strategy, the stratification is based on the individual risk of malignancy calculated with the IOTA 2-step strategy. Main Outcomes and Measures: Observed prevalence of malignancy in each O-RADS risk category, as well as sensitivity and specificity. The reference standard was the status of the tumor at inclusion, determined by histology or clinical and ultrasonographic follow-up for 1 year. Multiple imputation was used for uncertain outcomes owing to inconclusive follow-up information. Results: Median age of the 4905 patients was 48 years (IQR, 36-62 years). Data on race and ethnicity were not collected. A total of 3441 tumors (70%) were benign, 978 (20%) were malignant, and 486 (10%) had uncertain classification. Using the O-RADS lexicon resulted in 1.1% (24 of 2196) observed prevalence of malignancy in O-RADS 2, 4% (34 of 857) in O-RADS 3, 27% (246 of 904) in O-RADS 4, and 78% (732 of 939) in O-RADS 5; the corresponding results for the IOTA 2-step strategy were 0.9% (18 of 1984), 4% (58 of 1304), 30% (206 of 690), and 82% (756 of 927). At the 10% risk threshold (O-RADS 4-5), the O-RADS lexicon had 92% sensitivity (95% CI, 87%-96%) and 80% specificity (95% CI, 74%-85%), and the IOTA 2-step strategy had 91% sensitivity (95% CI, 84%-95%) and 85% specificity (95% CI, 80%-88%). Conclusions and Relevance: The findings of this external diagnostic validation study suggest that both the O-RADS lexicon and the IOTA 2-step strategy can be used to stratify patients into risk groups. However, the observed malignancy rate in O-RADS 2 was not clearly below 1%.

Experiences and well-being of healthcare professionals working in the field of ultrasound in obstetrics and gynaecology as the SARS-CoV-2 pandemic were evolving: a cross-sectional survey study.

OBJECTIVE: Assess experience of healthcare professionals (HCPs) working with ultrasound in obstetrics and gynaecology during the evolving SARS-CoV-2 pandemic, given the new and unprecedented challenges involving viral exposure, personal protective equipment (PPE) and well-being. DESIGN: Prospective cross-sectional survey study. SETTING: Online international survey. Single-best, open box and Hospital Anxiety and Depression Scale (HADS) questions. PARTICIPANTS: The survey was sent to 35 509 HCPs in 124 countries and was open from 7 to 21 May 2020. 2237/3237 (69.1%) HCPs from 115 countries who consented to participate completed the survey. 1058 (47.3%) completed the HADS. PRIMARY OUTCOME MEASURES: Overall prevalence of SARS-CoV-2, depression and anxiety among HCPs in relation to country and PPE availability. ANALYSES: Univariate analyses were used to investigate associations without generating erroneous causal conclusions. RESULTS: Confirmed/suspected SARS-CoV-2 prevalence was 13.0%. PPE provision concerns were raised by 74.1% of participants; highest among trainees/resident physicians (83.9%) and among HCPs in Spain (89.7%). Most participants worked in self-perceived high-risk areas with SARS-CoV-2 (67.5%-87.0%), with proportionately more trainees interacting with suspected/confirmed infected patients (57.1% vs 24.2%-40.6%) and sonographers seeing more patients who did not wear a mask (33.3% vs 13.9%-7.9%). The most frequent PPE combination used was gloves and a surgical mask (22.3%). UK and US respondents reported spending less time self-isolating (8.8 days) and lower satisfaction with their national pandemic response (37.0%-43.0%). 19.8% and 8.8% of respondents met the criteria for moderate to severe anxiety and depression, respectively. CONCLUSIONS: Reported prevalence of SARS-CoV-2 in HCPs is consistent with literature findings. Most respondents used gloves and a surgical mask, with a greater SARS-CoV-2 prevalence compared with those using 'full' PPE. HCPs with the least agency (trainees and sonographers) were not only more likely to see high-risk patients but also less likely to be protected. A fifth of respondents reported moderate to severe anxiety.

Nucleosome footprinting in plasma cell-free DNA for the pre-surgical diagnosis of ovarian cancer.

Fragmentation patterns of plasma cell-free DNA (cfDNA) are known to reflect nucleosome positions of cell types contributing to cfDNA. Based on cfDNA fragmentation patterns, the deviation in nucleosome footprints was quantified between diagnosed ovarian cancer patients and healthy individuals. Multinomial modeling was subsequently applied to capture these deviations in a per sample nucleosome footprint score. Validation was performed in 271 cfDNAs pre-surgically collected from women with an adnexal mass. We confirmed that nucleosome scores were elevated in invasive carcinoma patients, but not in patients with benign or borderline disease. Combining nucleosome scores with chromosomal instability scores assessed in the same cfDNA improved prediction of malignancy. Nucleosome scores were, however, more reliable to predict non-high-grade serous ovarian tumors, which are characterized by low chromosomal instability. These data highlight that compared to chromosomal instability, nucleosome footprinting provides a complementary and more generic read-out for pre-surgical diagnosis of invasive disease in women with adnexal masses.

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

The current review sums up the literature on the diagnostic performance of models to predict malignancy in adnexal masses and the ability of ultrasound to make a specific diagnosis in adnexal masses. A summary of the role of ultrasound in assessing the extension of malignant ovarian disease is also provided.

Refining Adjuvant Therapy for Endometrial Cancer: New Standards and Perspectives.

Endometrial carcinoma is the most frequent cancer of the reproductive female organs. Most endometrial cancers are diagnosed at early stage (75%). Treatment options depend on pathogenetic, histopathologic and clinical characteristic at the diagnosis. To improve patient management in the near future, recent research has focused on new molecular features; evidence has shown that these give a better definition of patient prognosis and can help in tailoring adjuvant treatments by identifying specific subgroups of patients whose tumors may benefit from specific therapeutic approaches. In this review, we will focus on current knowledge of adjuvant treatment of endometrial carcinoma, using a prognostic-risk group stratification based on pathogenetic, clinical and molecular features, and will take a look at the ongoing trials that will further change the therapeutic approach in coming years.

Gut microbiota and its influence on ovarian cancer carcinogenesis, anticancer therapy and surgical treatment: A literature review.

Ovarian cancer (OC) is the most lethal gynecological malignancy and very little is known about the underlying tumorigenesis mechanisms. For other tumors, like colorectal cancer, a relationship between several opportunistic pathogens and cancer development and progression has been proven. Recent researches also underline a possible correlation between gut microbiota dysbiosis and cancer treatment efficacy and adverse effects. Several studies have also demonstrated a link between abdominal surgery and gut microbiota modifications. In this paper, we aim to review the available evidences of this issue in OC to understand if there is a relationship between gut microbiota modifications and efficacy and adverse effects of cancer therapies, either surgical and medical treatments. Well-designed clinical studies, with a robust translational component, are required to better understand the modulation of gut microbiota during OC treatment. The microbiota/microbiome composition analysis, in the near future, could represent a novel instrument to personalize anticancer therapies.

Neo-Adjuvant Chemotherapy Reduces, and Surgery Increases Immunosuppression in First-Line Treatment for Ovarian Cancer.

In monotherapy, immunotherapy has a poor success rate in ovarian cancer. Upgrading to a successful combinatorial immunotherapy treatment implies knowledge of the immune changes that are induced by chemotherapy and surgery. METHODOLOGY: Patients with a new d ovarian cancer diagnosis underwent longitudinal blood samples at different time points during primary treatment. RESULTS: Ninety patients were included in the study (33% primary debulking surgery (PDS) with adjuvant chemotherapy (ACT), 61% neo-adjuvant chemotherapy (NACT) with interval debulking surgery (IDS), and 6% debulking surgery only). Reductions in immunosuppression were observed after NACT, but surgery reverted this effect. The immune-related proteins showed a pronounced decrease in immune stimulation and immunosuppression when primary treatment was completed. NACT with IDS leads to a transient amelioration of the immune microenvironment compared to PDS with ACT. CONCLUSION: The implementation of immunotherapy in the primary treatment schedule of ovarian cancer cannot be induced blindly. Carboplatin-paclitaxel seems to ameliorate the hostile immune microenvironment in ovarian cancer, which is less pronounced at the end of primary treatment. This prospective study during primary therapy for ovarian cancer that also looks at the evolution of immune-related proteins provides us with an insight into the temporary windows of opportunity in which to introduce immunotherapy during primary treatment.