Prognostic impact of suspicious extraabdominal lymph nodes on patient survival in advanced ovarian cancer.

OBJECTIVE: To evaluate the clinical impact of suspicious extra-abdominal lymph nodes (EALNs) identified preoperatively on CT and/or PET/CT images in advanced ovarian cancer. METHODS: A retrospective study was conducted with 122 patients diagnosed with stage III or IV ovarian cancer with preoperative CT and/or PET/CT images from 2006 to 2022. Imaging studies were evaluated for the presence, size and location of suspicious EALNs. Suspicious lymph node enlargement was defined by a cut-off ≥5mm short-axis dimension on CT and/or lesions with maximum standardized uptake values of ≥2.5 on PET/CT. This study only included patients who did not have their EALNs surgically removed. RESULTS: A total 109 patients met the inclusion criteria; 36 (33%) had suspicious EALNs and were categorized as "node-positive". The median overall survival (OS) was 45.73 months for the "node-positive" and 46.50 months for the "node-negative" patients (HR 1.17, 95% CI 0.68-2.00, p = 0.579). In multivariate analysis, after adjusting for other variables selected by process of backward elimination using a significance level of p<0.20, suspicious EALNs still showed no clinical significance on OS (aHR 1.20, 95% CI 0.67-2.13, p = 0.537) as well as progression-free survival (aHR 1.43, 95% CI 0.85-2.41, p = 0.174). Old age (aHR 2.23, 95% CI 1.28-3.89, p = 0.005) and platinum resistance (aHR 1.92, 95% CI 1.10-3.36, p = 0.023) affects adversely on OS. CONCLUSION: Suspicious EALNs did not worsen the prognosis of patients with advanced ovarian cancer. However, its impact on survival is not yet clarified. Further investigation is required to assess the clinical significance of suspicious EALNs on preoperative imaging studies.

I saw the "sea anemone" sign: Puffy face of borderline ovarian tumors.

The sea anemone sign is a radiologic sign seen on magnetic resonance imaging (MRI) studies that indicates the morphological development of serous borderline ovarian tumors (SBOTs), as papillary projections originating from the wall of the cystic lesion. The presence of T2 hypointense fibrous stroma in the center of the papilla is a helpful tip in the diagnosis of SBOTs. Those projections might also be assumed to have a frond-like appearance which can be seen as branching papillary projections, especially on T2-weighted imaging. The term "sea anemone" sign is described by Tanaka et al. who deemed it as a "hallmark" feature of surface SBOTs.

Mature cystic teratoma with co-existent mucinous cystadenocarcinoma: describing a diagnostic challenge-a case report.

BACKGROUND: Mature cystic teratoma co-existing with a mucinous cystadenocarcinoma is a rare tumor that few cases have been reported until now. In these cases, either a benign teratoma is malignantly transformed into adenocarcinoma or a collision tumor is formed between a mature cystic teratoma and a mucinous tumor, which is either primarily originated from epithelial-stromal surface of the ovary, or secondary to a primary gastrointestinal tract tumor. The significance of individualizing the two tumors has a remarkable effect on further therapeutic management. CASE PRESENTATION: In this case, a mature cystic teratoma is co-existed with a mucinous cystadenocarcinoma in the same ovary in a 33-year-old Iranian female. Computed Tomography (CT) Scan with additional contrast of the left ovarian mass suggested a teratoma, whereas examination of resected ovarian mass reported an adenocarcinoma with a cystic teratoma. A dermoid cyst with another multi-septate cystic lesion including mucoid material was revealed in the gross examination of the surgical specimen. Histopathological examination revealed a mature cystic teratoma in association with a well-differentiated mucinous cystadenocarcinoma. The latter showed a CK7-/CK20 + immune profile. Due to the lack of clinical, radiological, and biochemical discoveries attributed to a primary lower gastrointestinal tract tumor, the immune profile proposed the chance of adenocarcinomatous transformation of a benign teratoma. CONCLUSIONS: This case shows the significance of large sampling, precise recording of the gross aspects, histopathological examination, immunohistochemical analysis, and the help of radiological and clinical results to correctly diagnose uncommon tumors.

Borderline tumour recurrence: how quickly does the tumour grow?

This abstract describes a case of the growth of a serous borderline tumour recurrence and cyst to papillary projection ratio with associated ultrasound images. The aetiology, presentation and management of such cases are explored and compared to the literature.

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.

A novel NIR fluorescent probe for enhanced ß-galactosidase detection and tumor imaging in ovarian cancer models.

The advancement of biological imaging techniques critically depends on the development of novel near-infrared (NIR) fluorescent probes. In this study, we introduce a designed NIR fluorescent probe, NRO-ßgal, which exhibits a unique off-on response mechanism to ß-galactosidase (ß-gal). Emitting a fluorescence peak at a wavelength of 670 nm, NRO-ßgal showcases a significant Stokes shift of 85 nm, which is indicative of its efficient energy transfer and minimized background interference. The probe achieves a remarkably low in vitro detection limit of 0.2 U/L and demonstrates a rapid response within 10 min, thereby underscoring its exceptional sensitivity, selectivity, and operational swiftness. Such superior analytical performance broadens the horizon for its application in intricate biological imaging studies. To validate the practical utility of NRO-ßgal in bio-imaging, we employed ovarian cancer cell and mouse models, where the probe's efficacy in accurately delineating tumor cells was examined. The results affirm NRO-ßgal's capability to provide sharp, high-contrast images of tumor regions, thereby significantly enhancing the precision of surgical tumor resection. Furthermore, the probe's potential for real-time monitoring of enzymatic activity in living tissues underscores its utility as a powerful tool for diagnostics in oncology and beyond.

Prediction of ovarian cancer prognosis using statistical radiomic features of ultrasound images.

Objective. Ovarian cancer is the deadliest gynecologic malignancy worldwide. Ultrasound is the most useful non-invasive test for preoperative diagnosis of ovarian cancer. In this study, by leveraging multiple ultrasound images from the same patient to generate personalized, informative statistical radiomic features, we aimed to develop improved ultrasound image-based prognostic models for ovarian cancer.Approach. A total of 2057 ultrasound images from 514 ovarian cancer patients, including 355 patients with epithelial ovarian cancer, from two hospitals in China were collected for this study. The models were constructed using our recently developed Frequency Appearance in Multiple Univariate pre-Screening feature selection algorithm and Cox proportional hazards model.Main results. The models showed high predictive performance for overall survival (OS) and recurrence-free survival (RFS) in both epithelial and nonepithelial ovarian cancer, with concordance indices ranging from 0.773 to 0.794. Radiomic scores predicted 2 year OS and RFS risk groups with significant survival differences (log-rank test,P< 1.0 × 10-4for both validation cohorts). OS and RFS hazard ratios between low- and high-risk groups were 15.994 and 30.692 (internal cohort) and 19.339 and 19.760 (external cohort), respectively. The improved performance of these newly developed prognostic models was mainly attributed to the use of multiple preoperative ultrasound images from the same patient to generate statistical radiomic features, rather than simply using the largest tumor region of interest among them. The models also revealed that the roundness of tumor lesion shape was positively correlated with prognosis for ovarian cancer.Significance.The newly developed prognostic models based on statistical radiomic features from ultrasound images were highly predictive of the risk of cancer-related death and possible recurrence not only for patients with epithelial ovarian cancer but also for those with nonepithelial ovarian cancer. They thereby provide reliable, non-invasive markers for individualized prognosis evaluation and clinical decision-making for patients with ovarian cancer.

Histopathologic image-based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer.

Platinum-based chemotherapy is the cornerstone treatment for female high-grade serous ovarian carcinoma (HGSOC), but choosing an appropriate treatment for patients hinges on their responsiveness to it. Currently, no available biomarkers can promptly predict responses to platinum-based treatment. Therefore, we developed the Pathologic Risk Classifier for HGSOC (PathoRiCH), a histopathologic image-based classifier. PathoRiCH was trained on an in-house cohort (n = 394) and validated on two independent external cohorts (n = 284 and n = 136). The PathoRiCH-predicted favorable and poor response groups show significantly different platinum-free intervals in all three cohorts. Combining PathoRiCH with molecular biomarkers provides an even more powerful tool for the risk stratification of patients. The decisions of PathoRiCH are explained through visualization and a transcriptomic analysis, which bolster the reliability of our model's decisions. PathoRiCH exhibits better predictive performance than current molecular biomarkers. PathoRiCH will provide a solid foundation for developing an innovative tool to transform the current diagnostic pipeline for HGSOC.

PMFFNet: A hybrid network based on feature pyramid for ovarian tumor segmentation.

Ovarian cancer is a highly lethal malignancy in the field of oncology. Generally speaking, the segmentation of ovarian medical images is a necessary prerequisite for the diagnosis and treatment planning. Therefore, accurately segmenting ovarian tumors is of utmost importance. In this work, we propose a hybrid network called PMFFNet to improve the segmentation accuracy of ovarian tumors. The PMFFNet utilizes an encoder-decoder architecture. Specifically, the encoder incorporates the ViTAEv2 model to extract inter-layer multi-scale features from the feature pyramid. To address the limitation of fixed window size that hinders sufficient interaction of information, we introduce Varied-Size Window Attention (VSA) to the ViTAEv2 model to capture rich contextual information. Additionally, recognizing the significance of multi-scale features, we introduce the Multi-scale Feature Fusion Block (MFB) module. The MFB module enhances the network's capacity to learn intricate features by capturing both local and multi-scale information, thereby enabling more precise segmentation of ovarian tumors. Finally, in conjunction with our designed decoder, our model achieves outstanding performance on the MMOTU dataset. The results are highly promising, with the model achieving scores of 97.24%, 91.15%, and 87.25% in mACC, mIoU, and mDice metrics, respectively. When compared to several Unet-based and advanced models, our approach demonstrates the best segmentation performance.

Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours.

BACKGROUND: The timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS). METHODS: This study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC). RESULTS: The DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer-Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values. CONCLUSIONS: The US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS.

Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study.

BACKGROUND: Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours. METHODS: We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the maximum trimmed ovarian tumour sections were inputted into a pre-trained convolutional neural network (CNN) model. After internal enhancement and complex algorithms, each sample's predicted probability, known as the deep transfer learning signature (DTL_Sig), was generated. Clinical baseline data were analysed. Statistically significant clinical parameters and US semantic features in the training set were used to construct clinical signatures (Clinic_Sig). The prediction results of Rad_Sig, DTL_Sig, and Clinic_Sig for each sample were fused as new feature sets, to build the combined model, namely, the deep learning radiomic signature (DLR_Sig). We used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to estimate the performance of the multiclass classification model. RESULTS: The training set included 440 benign, 44 borderline, and 196 malignant ovarian tumours. The testing set included 109 benign, 11 borderline, and 49 malignant ovarian tumours. DLR_Sig three-class prediction model had the best overall and class-specific classification performance, with micro- and macro-average AUC of 0.90 and 0.84, respectively, on the testing set. Categories of identification AUC were 0.84, 0.85, and 0.83 for benign, borderline, and malignant ovarian tumours, respectively. In the confusion matrix, the classifier models of Clinic_Sig and Rad_Sig could not recognise borderline ovarian tumours. However, the proportions of borderline and malignant ovarian tumours identified by DLR_Sig were the highest at 54.55% and 63.27%, respectively. CONCLUSIONS: The three-class prediction model of US-based DLR_Sig can discriminate between benign, borderline, and malignant ovarian tumours. Therefore, it may guide clinicians in determining the differential management of patients with ovarian tumours.

Diagnostic value of ultrasonography in identifying unilateral ovarian luteoma in a dog.

Background: Diagnosing ovarian tumors in dogs can be challenging since the clinical symptoms are often generic. The present case report underscores a rare case in which a suspected unilateral ovarian tumor in a dog was initially identified using ultrasonography and subsequently confirmed to be a luteoma through postoperative histopathology. Case Description: An 8-year and 6-month-old female Maltese dog presented with a 10-day history of vulvovaginal bleeding, hematuria, and decreased appetite. Physical examination revealed only vaginal bleeding, with no other abnormalities. Laboratory examinations showed no abnormalities, while abdominal radiography revealed the presence of cystic calculi as the sole abnormality. Abdominal ultrasound revealed an enlarged right ovary with regular contour and echogenicity, featuring unusual cystic components surrounding the right ovarian parenchyma. Furthermore, irregular thickening with multiple cystic lesions was observed in the endometrial wall of the bilateral uterine horns, indicative of cystic endometrial hyperplasia. Ultrasonographic findings suggested unilateral right ovarian disease. During ovariohysterectomy, the right ovary was slightly larger than the left ovary and adhered to the surrounding mesenteric fat layer and right pancreatic parenchyma. Histopathological examination confirmed the diagnosis of luteoma in the right ovary. Three days after surgery, the patient's clinical signs exhibited complete improvement, with the return of normal appetite. Conclusion: This case report highlights a rare diagnosis of unilateral ovarian luteoma based on mild ultrasonographic abnormalities, which was ultimately confirmed on histopathological examination.

Customizable Porphyrin Platform Enables Folate Receptor PET Imaging Using Copper-64.

Folate receptors including folate receptor α (FRα) are overexpressed in up to 90% of ovarian cancers. Ovarian cancers overexpressing FRα often exhibit high degrees of drug resistance and poor outcomes. A porphyrin chassis has been developed that is readily customizable according to the desired targeting properties. Thus, compound O5 includes a free base porphyrin, two water-solubilizing groups that project above and below the macrocycle plane, and a folate targeting moiety. Compound O5 was synthesized (>95% purity) and exhibited aqueous solubility of at least 0.48 mM (1 mg/mL). Radiolabeling of O5 with 64Cu in HEPES buffer at 37 °C gave a molar activity of 1000 µCi/µg (88 MBq/nmol). [64Cu]Cu-O5 was stable in human serum for 24 h. Cell uptake studies showed 535 ± 12% bound/mg [64Cu]Cu-O5 in FRα-positive IGROV1 cells when incubated at 0.04 nM. Subcellular fractionation showed that most radioactivity was associated with the cytoplasmic (39.4 ± 2.7%) and chromatin-bound nuclear (53.0 ± 4.2%) fractions. In mice bearing IGROV1 xenografts, PET imaging studies showed clear tumor uptake of [64Cu]Cu-O5 from 1 to 24 h post injection with a low degree of liver uptake. The tumor standardized uptake value at 24 h post injection was 0.34 ± 0.16 versus 0.06 ± 0.07 in the blocking group. In summary, [64Cu]Cu-O5 was synthesized at high molar activity, was stable in serum, exhibited high binding to FRα-overexpressing cells with high nuclear translocation, and gave uptake that was clearly visible in mouse tumor xenografts.

Accuracy of machine learning in the preoperative identification of ovarian borderline tumors: a meta-analysis.

AIM: The objective of this study is to explore the diagnostic value of machine learning (ML) in borderline ovarian tumors through meta-analysis. METHODS: Pubmed, Embase, Web of Science, and Cochrane Library databases were comprehensively retrieved from database inception untill February 16, 2023. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was adopted to evaluate the risk of bias in the original studies. Sub-group analyses of ML were conducted according to clinical features and radiomics features. We separately discussed the discriminative value of ML for borderline vs benign and borderline vs malignant tumors. RESULTS: Eighteen studies involving 12,778 subjects were included in our analysis. The modeling variables mainly consisted of radiomics features (n=13) and a small number of clinical features (n=5). When distinguishing between borderline and benign tumors, the ML model based on radiomic features achieved a c-index of 0.782 (95% CI: 0.732-0.831), sensitivity of 0.75 (95% CI: 0.67-0.82), and specificity of 0.75 (95% CI: 0.67-0.81) in the validation set. When distinguishing between borderline and malignant tumors, the ML model based on radiomic features achieved a c-index of 0.916 (95% CI: 0.891-0.940), sensitivity of 0.86 (95% CI: 0.78-0.91), and specificity of 0.88 (95% CI: 0.82-0.92) in the validation set. In addition, we analyzed the discriminatory ability of radiologists and found that their sensitivity was 0.26 (95% CI: 0.12-0.46) and specificity was 0.94 (95% CI: 0.90-0.97). CONCLUSIONS: ML has tremendous potential in the preoperative diagnosis and differentiation of borderline ovarian tumors and may be more accurate than radiologists in diagnosing and differentiating borderline ovarian tumors.

A quantitative MRI-based approach to estimate the permeation and retention of nanomedicines in tumors.

Despite the potential of the enhanced permeability and retention (EPR) effect in tumor passive targeting, many nanotherapeutics have failed to produce meaningful clinical outcomes due to the variable and challenging nature of the tumor microenvironment (TME) and EPR effect. This EPR variability across tumors and inconsistent translation of nanomedicines from preclinical to clinical settings necessitates a reliable method to assess its presence in individual tumors. This study aimed to develop a reliable and non-invasive approach to estimate the EPR effect in tumors using a clinically compatible quantitative magnetic resonance imaging (qMRI) technique combined with a nano-sized MRI contrast agent. A quantitative MR imaging was developed using a dynamic contrast-enhanced (DCE) MRI protocol. Then, the permeability and retention of the nano-sized MRI contrast agent were evaluated in three different ovarian xenograft tumor models. Results showed significant differences in EPR effects among the tumor models, with tumor growth influencing the calculated parameters of permeability (Ktrans) and retention (Ve) based on Tofts pharmacokinetic (PK) modeling. Our data indicate that the developed quantitative DCE-MRI method, combined with the Tofts PK modeling, provides a robust and non-invasive approach to screen tumors for their responsiveness to nanotherapeutics. These results imply that the developed qMRI method can be beneficial for personalized cancer treatments by ensuring that nanotherapeutics are administered only to patients with tumors showing sufficient EPR levels.

ß-Galactosidase-activated near-infrared AIEgen for ovarian cancer imaging in vivo.

Near-infrared (NIR) aggregation induced-emission luminogens (AIEgens) circumvent the noisome aggregation-caused quenching (ACQ) effect in physiological milieu, thus holding high promise for real-time and sensitive imaging of biomarkers in vivo. ß-Galactosidase (ß-Gal) is a biomarker for primary ovarian carcinoma, but current AIEgens for ß-Gal sensing display emissions in the visible region and have not been applied in vivo. We herein propose an NIR AIEgen QM-TPA-Gal and applied it for imaging ß-Gal activity in vitro and in ovarian tumor model. After being internalized by ovarian cancer cells (e.g., SKOV3), the hydrophilic nonfluorescent QM-TPA-Gal undergoes hydrolyzation by ß-Gal to yield hydrophobic QM-TPA-OH, which subsequently aggregates into nanoparticles to turn NIR fluorescence "on" through the AIE mechanism. In vitro experimental results indicate that QM-TPA-Gal has a sensitive and selective response to ß-Gal with a limit of detection (LOD) of 0.21 U/mL. Molecular docking simulation confirms that QM-TPA-Gal has a good binding ability with ß-Gal to allow efficient hydrolysis. Furthermore, QM-TPA-Gal is successfully applied for ß-Gal imaging in SKOV3 cell and SKOV3-bearing living mouse models. It is anticipated that QM-TPA-Gal could be applied for early diagnosis of ovarian cancers or other ß-Gal-associated diseases in near future.

Developing a novel image marker to predict the clinical outcome of neoadjuvant chemotherapy (NACT) for ovarian cancer patients.

OBJECTIVE: Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the clinical outcomes to NACT vary significantly among different subgroups. Partial responses to NACT may lead to suboptimal debulking surgery, which will result in adverse prognosis. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy prognosis prediction of NACT at an early stage. METHODS: For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. This cluster was used as input for developing and optimizing support vector machine (SVM) based classifiers, which indicated the likelihood of receiving suboptimal cytoreduction after the NACT treatment. Two different kernels for SVM algorithm were explored and compared. A total of 42 ovarian cancer cases were retrospectively collected to validate the scheme. A nested leave-one-out cross-validation framework was adopted for model performance assessment. RESULTS: The results demonstrated that the model with a Gaussian radial basis function kernel SVM yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.806 ± 0.078. Meanwhile, this model achieved overall accuracy (ACC) of 83.3%, positive predictive value (PPV) of 81.8%, and negative predictive value (NPV) of 83.9%. CONCLUSION: This study provides meaningful information for the development of radiomics based image markers in NACT treatment outcome prediction.

Development and validation of an interpretable model integrating multimodal information for improving ovarian cancer diagnosis.

Ovarian cancer, a group of heterogeneous diseases, presents with extensive characteristics with the highest mortality among gynecological malignancies. Accurate and early diagnosis of ovarian cancer is of great significance. Here, we present OvcaFinder, an interpretable model constructed from ultrasound images-based deep learning (DL) predictions, Ovarian-Adnexal Reporting and Data System scores from radiologists, and routine clinical variables. OvcaFinder outperforms the clinical model and the DL model with area under the curves (AUCs) of 0.978, and 0.947 in the internal and external test datasets, respectively. OvcaFinder assistance led to improved AUCs of radiologists and inter-reader agreement. The average AUCs were improved from 0.927 to 0.977 and from 0.904 to 0.941, and the false positive rates were decreased by 13.4% and 8.3% in the internal and external test datasets, respectively. This highlights the potential of OvcaFinder to improve the diagnostic accuracy, and consistency of radiologists in identifying ovarian cancer.

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.

Current development of theragnostic nanoparticles for women's cancer treatment.

In the biomedical industry, nanoparticles (NPs-exclusively small particles with size ranging from 1-100 nanometres) are recently employed as powerful tools due to their huge potential in sophisticated and enhanced cancer theragnostic (i.e. therapeutics and diagnostics). Cancer is a life-threatening disease caused by carcinogenic agents and mutation in cells, leading to uncontrolled cell growth and harming the body's normal functioning while affecting several factors like low levels of reactive oxygen species, hyperactive antiapoptotic mRNA expression, reduced proapoptotic mRNA expression, damaged DNA repair, and so on. NPs are extensively used in early cancer diagnosis and are functionalized to target receptors overexpressing cancer cells for effective cancer treatment. This review focuses explicitly on how NPs alone and combined with imaging techniques and advanced treatment techniques have been researched against 'women's cancer' such as breast, ovarian, and cervical cancer which are substantially occurring in women. NPs, in combination with numerous imaging techniques (like PET, SPECT, MRI, etc) have been widely explored for cancer imaging and understanding tumor characteristics. Moreover, NPs in combination with various advanced cancer therapeutics (like magnetic hyperthermia, pH responsiveness, photothermal therapy, etc), have been stated to be more targeted and effective therapeutic strategies with negligible side effects. Furthermore, this review will further help to improve treatment outcomes and patient quality of life based on the theragnostic application-based studies of NPs in women's cancer treatment.