He Zhang

Polymer‐PARPi Conjugates Delivering USP1i for Maximizing Synthetic Lethality to Stimulate STING Pathway in High‐Grade Serous Ovarian Cancer

Abstract Overcoming the immunosuppressive tumor microenvironment and therapeutic resistance remains a significant challenge in ovarian cancer treatment. In this study, a glutathione‐responsive polymeric nanoparticle platform for the co‐delivery of the USP1 inhibitor SJB3‐019A and the PARP inhibitor Niraparib is developed. This system synergistically enhances DNA damage accumulation, suppresses homologous recombination repair, and robustly activate the STING signaling pathway, leading to enhanced type I interferon responses and mitigation of immune evasion. Mechanistically, USP1 inhibition significantly impairs DNA repair, amplifying the synthetic lethality of PARP inhibition and promoting immunogenic cell death. In vivo studies demonstrated precise, glutathione‐responsive drug release and substantial tumor accumulation of the nanoparticles, resulting in remarkable antitumor efficacy in murine ovarian cancer models. Importantly, this combinational approach effectively remodels the tumor microenvironment by increasing CD8⁺ T cell infiltration and enhancing tumor sensitivity to immune checkpoint blockade therapies. This innovative strategy targeting DNA damage responses presents a promising platform for precise and effective ovarian cancer immunotherapy.

Clinicopathological factors of ovarian clear cell carcinoma: A single institutional analysis of 247 cases in China

Ovarian clear cell carcinoma (OCCC) is a subtype of ovarian cancer with a poor prognosis that often shows resistance to chemotherapy. This study retrospectively analyzed 247 patients with OCCC who were admitted to the Cancer Hospital of the Chinese Academy of Medical Sciences (CAMS) between August 2007 and August 2023. Univariate and multivariate Cox regression analyses were used to identify clinicopathological factors associated with OCCC, and a nomogram prediction model was developed to predict OCCC patient survival outcomes. Kaplan‒Meier survival analysis was used to compare survival outcomes among patients with recurrent disease. Compared with systemic therapy, secondary debulking surgery significantly improved the postrecurrence survival (PRS) rate (P = 0.006). Subgroup analysis revealed that the survival benefit was more pronounced in patients with recurrence and satisfactory tumor shrinkage (PPRS = 0.01, PPFS2 = 0.047). The multivariate analysis revealed that positive preoperative ascites, incomplete remission following initial treatment, and undergoing more than six cycles of postoperative chemotherapy were independent prognostic factors affecting overall survival (OS). Additionally, patients with a positive PD-L1 test who received immunotherapy did not experience relapse during the follow-up period. In conclusion, the secondary clearance procedure offers significant benefits for patients with recurrent OCCC, and patients may experience a survival benefit from supplemental immune or targeted therapy at the end of chemotherapy. The development of a personalized treatment plan can help achieve precise treatment, improve prognosis, and enhance patients' quality of life.

Fully Automated Identification of Lymph Node Metastases and Lymphovascular Invasion in Endometrial Cancer From Multi‐Parametric MRI by Deep Learning

BackgroundEarly and accurate identification of lymphatic node metastasis (LNM) and lymphatic vascular space invasion (LVSI) for endometrial cancer (EC) patients is important for treatment design, but difficult on multi‐parametric MRI (mpMRI) images.PurposeTo develop a deep learning (DL) model to simultaneously identify of LNM and LVSI of EC from mpMRI images.Study TypeRetrospective.PopulationSix hundred twenty‐one patients with histologically proven EC from two institutions, including 111 LNM‐positive and 168 LVSI‐positive, divided into training, internal, and external test cohorts of 398, 169, and 54 patients, respectively.Field Strength/SequenceT2‐weighted imaging (T2WI), contrast‐enhanced T1WI (CE‐T1WI), and diffusion‐weighted imaging (DWI) were scanned with turbo spin‐echo, gradient‐echo, and two‐dimensional echo‐planar sequences, using either a 1.5 T or 3 T system.AssessmentEC lesions were manually delineated on T2WI by two radiologists and used to train an nnU‐Net model for automatic segmentation. A multi‐task DL model was developed to simultaneously identify LNM and LVSI positive status using the segmented EC lesion regions and T2WI, CE‐T1WI, and DWI images as inputs. The performance of the model for LNM‐positive diagnosis was compared with those of three radiologists in the external test cohort.Statistical TestsDice similarity coefficient (DSC) was used to evaluate segmentation results. Receiver Operating Characteristic (ROC) analysis was used to assess the performance of LNM and LVSI status identification. P value <0.05 was considered significant.ResultsEC lesion segmentation model achieved mean DSC values of 0.700 ± 0.25 and 0.693 ± 0.21 in the internal and external test cohorts, respectively. For LNM positive/LVSI positive identification, the proposed model achieved AUC values of 0.895/0.848, 0.806/0.795, and 0.804/0.728 in the training, internal, and external test cohorts, respectively, and better than those of three radiologists (AUC = 0.770/0.648/0.674).Data ConclusionThe proposed model has potential to help clinicians to identify LNM and LVSI status of EC patients and improve treatment planning.Evidence Level3Technical EfficacyStage 2

Determination of p53abn endometrial cancer: a multitask analysis using radiological-clinical nomogram on MRI

Abstract Objectives We aimed to differentiate endometrial cancer (EC) between TP53mutation (P53abn) and Non-P53abn subtypes using radiological-clinical nomogram on EC body volume MRI. Methods We retrospectively recruited 227 patients with pathologically proven EC from our institution. All these patients have undergone molecular pathology diagnosis based on the Cancer Genome Atlas. Clinical characteristics and histological diagnosis were recorded from the hospital information system. Radiomics features were extracted from online Pyradiomics processors. The diagnostic performance across different acquisition protocols was calculated and compared. The radiological-clinical nomogram was established to determine the nonendometrioid, high-risk, and P53abn EC group. Results The best MRI sequence for differentiation P53abn from the non-P53abn group was contrast-enhanced T1WI (test AUC: 0.8). The best MRI sequence both for differentiation endometrioid cancer from nonendometrioid cancer and high-risk from low- and intermediate-risk groups was apparent diffusion coefficient map (test AUC: 0.665 and 0.690). For all 3 tasks, the combined model incorporating all the best discriminative features from each sequence yielded the best performance. The combined model achieved an AUC of 0.845 in the testing cohorts for P53abn cancer identification. The MR-based radiomics diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC: 0.834 vs 0.682). Conclusion In the present study, the diagnostic model based on the combination of both radiomics and clinical features yielded a higher performance in differentiating nonendometrioid and P53abn cancer from other EC molecular subgroups, which might help design a tailed treatment, especially for patients with high-risk EC. Advances in knowledge (1) The contrast-enhanced T1WI was the best MRI sequence for differentiation P53abn from the non-P53abn group (test AUC: 0.8). (2) The radiomics-based diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC: 0.834 vs 0.682). (3) The proposed model derived from multi-parametric MRI images achieved a higher accuracy in P53abn EC identification (AUC: 0.845).

Machine Learning for Preoperative Assessment and Postoperative Prediction in Cervical Cancer: Multicenter Retrospective Model Integrating MRI and Clinicopathological Data

Abstract Background Machine learning (ML) has been increasingly applied to cervical cancer (CC) research. However, few studies have combined both clinical parameters and imaging data. At the same time, there remains an urgent need for more robust and accurate preoperative assessment of parametrial invasion and lymph node metastasis, as well as postoperative prognosis prediction. Objective The objective of this study is to develop an integrated ML model combining clinicopathological variables and magnetic resonance image features for (1) preoperative parametrial invasion and lymph node metastasis detection and (2) postoperative recurrence and survival prediction. Methods Retrospective data from 250 patients with CC (2014‐2022; 2 tertiary hospitals) were analyzed. Variables were assessed for their predictive value regarding parametrial invasion, lymph node metastasis, survival, and recurrence using 7 ML models: K-nearest neighbor (KNN), support vector machine, decision tree, random forest (RF), balanced RF, weighted DT, and weighted KNN. Performance was assessed via 5-fold cross-validation using accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic curve (AUC). The optimal models were deployed in an artificial intelligence–assisted contouring and prognosis prediction system. Results Among 250 women, there were 11 deaths and 24 recurrences. (1) For preoperative evaluation, the integrated model using balanced RF achieved optimal performance (sensitivity 0.81, specificity 0.85) for parametrial invasion, while weighted KNN achieved the best performance for lymph node metastasis (sensitivity 0.98, AUC 0.72). (2) For postoperative prognosis, weighted KNN also demonstrated high accuracy for recurrence (accuracy 0.94, AUC 0.86) and mortality (accuracy 0.97, AUC 0.77), with relatively balanced sensitivity of 0.80 and 0.33, respectively. (3) An artificial intelligence–assisted contouring and prognosis prediction system was developed to support preoperative evaluation and postoperative prognosis prediction. Conclusions The integration of clinical data and magnetic resonance images provides enhanced diagnostic capability to preoperatively detect parametrial invasion and lymph node metastasis detection and prognostic capability to predict recurrence and mortality for CC, facilitating personalized, precise treatment strategies.

MR-based radiomics-clinical nomogram in epithelial ovarian tumor prognosis prediction: tumor body texture analysis across various acquisition protocols

Abstract Background Epithelial ovarian cancer (EOC) is the most malignant gynecological tumor in women. This study aimed to construct and compare radiomics-clinical nomograms based on MR images in EOC prognosis prediction. Methods A total of 186 patients with pathologically proven EOC were enrolled and randomly divided into a training cohort (n = 130) and a validation cohort (n = 56). Clinical characteristics of each patient were retrieved from the hospital information system. A total of 1116 radiomics features were extracted from tumor body on T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (CE-T1WI). Paired sequence signatures were constructed, selected and trained to build a prognosis prediction model. Radiomic-clinical nomogram was constructed based on multivariate logistic regression analysis with radiomics score and clinical features. The predictive performance was evaluated by receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA) and calibration curve. Results The T2WI radiomic-clinical nomogram achieved a favorable prediction performance in the training and validation cohort with an area under ROC curve (AUC) of 0.866 and 0.818, respectively. The DCA showed that the T2WI radiomic-clinical nomogram was better than other models with a greater clinical net benefit. Conclusion MR-based radiomics analysis showed the high accuracy in prognostic estimation of EOC patients and could help to predict therapeutic outcome before treatment.