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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Development and validation of an ultrasound­based radiomics nomogram to predict lymph node status in patients with high-grade serous ovarian cancer: a retrospective analysis.

BACKGROUND: Despite advances in medical imaging technology, the accurate preoperative prediction of lymph node status remains challenging in ovarian cancer. This retrospective study aimed to investigate the feasibility of using ultrasound-based radiomics combined with preoperative clinical characteristics to predict lymph node metastasis (LNM) in patients with high-grade serous ovarian cancer (HGSOC). RESULTS: Patients with 401 HGSOC lesions from two institutions were enrolled: institution 1 for the training cohort (n = 322) and institution 2 for the external test cohort (n = 79). Radiomics features were extracted from the three preoperative ultrasound images of each lesion. During feature selection, primary screening was first performed using the sample variance F-value, followed by recursive feature elimination (RFE) to filter out the 12 most significant features for predicting LNM. The radscore derived from these 12 radiomic features and three clinical characteristics were used to construct a combined model and nomogram to predict LNM, and subsequent 10-fold cross-validation was performed. In the test phase, the three models were tested with external test cohort. The radiomics model had an area under the curve (AUC) of 0.899 (95% confidence interval [CI]: 0.864-0.933) in the training cohort and 0.855 (95%CI: 0.774-0.935) in the test cohort. The combined model showed good calibration and discrimination in the training cohort (AUC = 0.930) and test cohort (AUC = 0.881), which were superior to those of the radiomic and clinical models alone. CONCLUSIONS: The nomogram consisting of the radscore and preoperative clinical characteristics showed good diagnostic performance in predicting LNM in patients with HGSOC. It may be used as a noninvasive method for assessing the lymph node status in these patients.

MR diagnosis of SCC arising within ovarian cystic teratomas: analysis of mural nodule characteristics.

PURPOSE: This study aims to evaluate and identify magnetic resonance (MR) findings of mural nodules to detect squamous cell carcinoma arising from ovarian mature cystic teratoma (SCC-MCT). METHODS: This retrospective study examined 135 patients (SCC-MCTs, n = 12; and benign MCTs, n = 123) with confirmed diagnoses across five different institutions between January 2010 and June 2022. Preoperative MR images for each patient were independently assessed by two experienced radiologists and analyzed following previously reported findings (PRFs): age, tumor size, presence of mural nodules, size of mural nodule, and the angle between mural nodule and cyst wall (acute or obtuse). Furthermore, this study evaluated four mural nodule features-diffusion restriction, fat intensity, Palm tree appearance, and calcification-and the presence of transmural extension. RESULTS: There were significant differences between the SCC-MCT and benign MCT groups in terms of all PRFs and all mural nodule findings (p < 0.01). Among the PRFs, "tumor size" demonstrated the highest diagnostic performance, with a sensitivity of 83.3% and a specificity of 88.6%. A combination of the aforementioned four mural nodule findings showed a sensitivity and specificity of 83.3% and 97.6%, respectively, for the diagnosis of SCC-MCT. Regarding diagnosis based on a combination of four mural nodule findings, the specificity was significantly higher than the diagnosis based on tumor size (p = 0.021). Based on these mural nodule findings, three SCC-MCT patients without transmural invasion could be diagnosed. CONCLUSION: Mural nodule MR findings had a higher diagnostic performance than PRFs for SCC-MCT and can potentially allow early detection of SCC-MCTs.

A chalcone-based ESIPT and AIE fluorophore for ß-gal imaging in living cells.

ß-Galactosidase (ß-gal), which is responsible for the hydrolysis of the glycosidic bond of lactose to galactose, has been recognized as an important biomarker of cell or organism status, especially cell senescence and primary ovarian cancer. Extensive efforts have been devoted to develop probes for detecting and visualizing ß-gal in cells. Herein, a fluorescent probe gal-HCA which possesses both excited-state intramolecular proton transfer (ESIPT) and aggregation-induced emission (AIE) properties was prepared to monitor ß-gal in living cells. The probe consists of 2-hydroxy-4'-dimethylamino-chalcone (HCA) capped with a D-galactose group. The cleavage of the glycosidic bond in gal-HCA triggered by ß-gal releases HCA, which results in a significant bathochromic shift in fluorescence from 532 to 615 nm. The probe exhibited high selectivity and sensitivity toward ß-gal with a detection limit as low as 0.0122 U mL-1. The confocal imaging investigation demonstrated the potential of gal-HCA in monitoring the endocellular overexpressed ß-gal in senescent cells and ovarian cancer cells. This study provides a straightforward approach for the development of fluorescent probes to monitor ß-gal and detection of ß-gal-associated diseases.

Cutaneous Metastasis of Ovarian Cancer on 68 Ga-FAPI PET/CT.

ABSTRACT: Ovarian cancer with cutaneous metastases is quite rare. We report the findings of cutaneous metastases from ovarian cancer on 68 Ga-FAPI PET/CT imaging. A 53-year-old woman with cutaneous metastases from ovarian cancer was enrolled in 68 Ga-FAPI PET/CT clinical trial. The images showed intense FAPI activity in the known cutaneous metastases.