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.

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.

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.

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.

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.

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.

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.

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

BACKGROUND: Preoperative prediction of International Federation of Gynecology and Obstetrics (FIGO) stage in patients with epithelial ovarian cancer (EOC) is crucial for determining appropriate treatment strategy. This study aimed to explore the value of contrast-enhanced CT (CECT) radiomics in predicting preoperative FIGO staging of EOC, and to validate the stability of the model through an independent external dataset. METHODS: A total of 201 EOC patients from three centers, divided into a training cohort (n = 106), internal (n = 46) and external (n = 49) validation cohorts. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for screening radiomics features. Five machine learning algorithms, namely logistic regression, support vector machine, random forest, light gradient boosting machine (LightGBM), and decision tree, were utilized in developing the radiomics model. The optimal performing algorithm was selected to establish the radiomics model, clinical model, and the combined model. The diagnostic performances of the models were evaluated through receiver operating characteristic analysis, and the comparison of the area under curves (AUCs) were conducted using the Delong test or F-test. RESULTS: Seven optimal radiomics features were retained by the LASSO algorithm. The five radiomics models demonstrate that the LightGBM model exhibits notable prediction efficiency and robustness, as evidenced by AUCs of 0.83 in the training cohort, 0.80 in the internal validation cohort, and 0.68 in the external validation cohort. The multivariate logistic regression analysis indicated that carcinoma antigen 125 and tumor location were identified as independent predictors for the FIGO staging of EOC. The combined model exhibited best diagnostic efficiency, with AUCs of 0.95 in the training cohort, 0.83 in the internal validation cohort, and 0.79 in the external validation cohort. The F-test indicated that the combined model exhibited a significantly superior AUC value compared to the radiomics model in the training cohort (P < 0.001). CONCLUSIONS: The combined model integrating clinical characteristics and radiomics features shows potential as a non-invasive adjunctive diagnostic modality for preoperative evaluation of the FIGO staging status of EOC, thereby facilitating clinical decision-making and enhancing patient outcomes.

Development and validation of an 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.

Presentation and treatment of two cases of malignant struma ovarii.

BACKGROUND: Malignant Struma Ovarii (MSO) is a rare type of germ cell tumour which is diagnosed postoperatively on surgical pathology specimens by the presence of differentiated thyroid cancer in mature cystic teratomas in the ovaries. Treatment and follow-up procedures are not clearly established due to the paucity of MSO cases. CASE 1: A 44-year-old multiparous female presented with an irregular period. Ultrasound showed a left ovarian lesion mostly a dermoid cyst, however, CT showed a 3.8 × 2.7 × 4 cm complex cystic lesion with thick septation and enhancing soft tissue component. Laparoscopic left salpingo-oophorectomy was performed and histopathology showed a follicular variant of papillary thyroid carcinoma arising in a mature cystic teratoma. Peritoneal cytology was positive for malignancy. A thyroid function test was normal before surgery. Total thyroidectomy was performed followed by radioactive (RAI) iodine therapy. Later, a total laparoscopic hysterectomy and right salpingo-oophorectomy were performed. There is no evidence of recurrent disease during the 26-months follow-up. CASE 2: A 46-year-old single female presented with left lower abdominal pain that had persisted for 2 months. Imaging revealed an 8 × 9 × 9.5 cm left ovarian mass. Laparoscopic left salpingo-oophorectomy was performed and histopathology showed mature cystic teratoma with small papillary thyroid cancer. CT showed no evidence of metastatic disease. Later, the patient had a total thyroidectomy followed by radioactive (RAI) iodine therapy. She was started on thyroxine and later had total abdominal hysterectomy and right salpingo-oophorectomy. CONCLUSION: MSO is a very rare tumour. Preoperative diagnosis is very difficult because of the nonspecific symptoms and the lack of specific features in imaging studies. Also, there is no consensus on the optimal treatment of women with MSO. Our two cases add to the limited number of MSO cases.

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.

Hydropic leiomyoma-like ovarian tumor: a case report.

Uterine leiomyomas, benign tumors common in reproductive-aged women, can display rare variants such as hydropic leiomyoma (HL), which exhibit unique histological features like zonal edema and increased vascularity. However, due to its rarity, comprehensive clinical knowledge about HL is limited. We report a case of a 49-year-old Japanese woman who was premenopausal and nulliparous, presenting with a two-year history of abdominal distension. An MRI scan revealed a 20 cm mass in the posterior part of the uterus, exhibiting characteristics suggestive of an ovarian tumor. During laparotomy, a cystic tumor connected with a swollen fibroid was found, and pathology confirmed HL. This case emphasizes that hydropic leiomyomas can mimic malignant tumors on ultrasonography due to their atypical features, necessitating additional evaluations using alternative imaging techniques or histopathological examinations for accurate diagnosis and appropriate management. The patient recovered uneventfully, broadening our understanding of HL's clinical presentation.

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.

Interrogating the Theranostic Capacity of a MUC16-Targeted Antibody for Ovarian Cancer.

Aberrantly expressed glycans on mucins such as mucin-16 (MUC16) are implicated in the biology that promotes ovarian cancer (OC) malignancy. Here, we investigated the theranostic potential of a humanized antibody, huAR9.6, targeting fully glycosylated and hypoglycosylated MUC16 isoforms. Methods: In vitro and in vivo targeting of the diagnostic radiotracer [89Zr]Zr-DFO-huAR9.6 was investigated via binding experiments, immuno-PET imaging, and biodistribution studies on OC mouse models. Ovarian xenografts were used to determine the safety and efficacy of the therapeutic version, [177Lu]Lu-CHX-A″-DTPA-huAR9.6. Results: In vivo uptake of [89Zr]Zr-DFO-huAR9.6 supported in vitro-determined expression levels: high uptake in OVCAR3 and OVCAR4 tumors, low uptake in OVCAR5 tumors, and no uptake in OVCAR8 tumors. Accordingly, [177Lu]Lu-CHX-A″-DTPA-huAR9.6 displayed strong antitumor effects in the OVCAR3 model and improved overall survival in the OVCAR3 and OVCAR5 models in comparison to the saline control. Hematologic toxicity was transient in both models. Conclusion: PET imaging of OC xenografts showed that [89Zr]Zr-DFO-huAR9.6 delineated MUC16 expression levels, which correlated with in vitro results. Additionally, we showed that [177Lu]Lu-CHX-A″-DTPA-huAR9.6 displayed strong antitumor effects in highly MUC16-expressing tumors. These findings demonstrate great potential for 89Zr- and 177Lu-labeled huAR9.6 as theranostic tools for the diagnosis and treatment of OC.

Sentinel Node Mapping in Ovarian Tumors: A Study Using Lymphoscintigraphy and SPECT/CT.

Purpose: Ovarian cancer in the early stage requires a complete surgical staging, including radical lymphadenectomy, implying subsequent risk of morbidity and complications. Sentinel lymph node (SLN) mapping is a procedure that attempts to reduce radical lymphadenectomy-related complications and morbidities. Our study evaluates the feasibility of SLN mapping in patients with ovarian tumors by the use of intraoperative Technetium-99m-Phytate (Tc-99m-Phytate) and postoperative lymphoscintigraphy using tomographic (single-photon emission computed tomography/computed tomography (SPECT/CT)) acquisition. Materials and Methods: Thirty-two patients with ovarian mass participated in this study. Intraoperative injection of the radiopharmaceutical was performed just after laparotomy and before the removal of tumor in utero-ovarian and suspensory ligaments of the ovary just beneath the peritoneum. Subsequently, pelvic and para-aortic lymphadenectomy was performed for malignant masses, and the presence of tumor in the lymph nodes was assessed through histopathological examination. Conversely, lymphadenectomy was not performed in patients with benign lesions or borderline ovarian tumors. Lymphoscintigraphy was performed within 24 hr using tomographic acquisition (SPECT/CT) of the abdomen and pelvis. Results: Final pathological examination showed 19 patients with benign pathology, 5 with borderline tumors, and 6 with malignant ovarian tumors. SPECT/CT identified SLNs in para-aortic-only areas in 6 (20%), pelvic/para-aortic areas in 14 (47%), and pelvic-only areas in 7 (23%) cases. Notably, additional unusual SLN locations were revealed in perirenal, intergluteal, and posterior to psoas muscle regions in three patients. We were not able to calculate the false negative rate due to the absence of patients with involved lymph nodes. Conclusion: SLN mapping using intraoperative injection of radiotracers is safe and feasible. Larger studies with more malignant cases are needed to better evaluate the sensitivity of this method for lymphatic staging of ovarian malignancies.

Mucin-producing tumors of the ovary--preoperative differentiation between metastatic ovarian mucinous carcinoma and primary mucinous malignant tumors.

OBJECTIVE: To investigate the clinical and magnetic resonance imaging (MRI) features for preoperatively discriminating  primary ovarian mucinous malignant tumors (POMTs) and metastatic mucinous carcinomas involving the ovary (MOMCs). METHODS: This retrospective multicenter study enrolled 61 patients with 22 POMTs and 49 MOMCs, which were pathologically proved between November 2014 to Jane 2023. The clinical and MRI features were evaluated and compared between POMTs and MOMCs. Univariate and multivariate analyses were performed to identify the significant variables between the two groups, which were then incorporated into a predictive nomogram, and ROC curve analysis was subsequently carried out to evaluate diagnostic performance. RESULTS: 35.9% patients with MOMCs were discovered synchronously with the primary carcinomas; 25.6% patients with MOMCs were bilateral, and all of the patients with POMTs were unilateral. The biomarker CEA was significantly different between the two groups (p = 0.002). There were significant differences in the following MRI features: tumor size, configuration, enhanced pattern, the number of cysts, honeycomb sign, stained-glass appearance, ascites, size diversity ratio, signal diversity ratio. The locular size diversity ratio (p = 0.005, OR = 1.31), and signal intensity diversity ratio (p = 0.10, OR = 4.01) were independent predictors for MOMCs. The combination of above independent criteria yielded the largest area under curve of 0.922 with a sensitivity of 82.3% and specificity of 88.9%. CONCLUSIONS: Patients with MOMCs were more commonly bilaterally and having higher levels of CEA, but did not always had a malignant tumor history. For ovarian mucin-producing tumors, the uniform locular sizes and signal intensities were more predict MOMCs.

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.

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.